Le Chat
The multilingual AI assistant powered by Europe's premier frontier models.
A Generative Model for Code with Bidirectional Infilling and Program Synthesis capabilities.
InCoder, developed by Meta AI Research, represents a significant leap in the evolution of code-centric large language models. Unlike traditional autoregressive models that generate code purely from left-to-right, InCoder is architected to perform bidirectional code infilling. By training on a specialized causal masking objective, the model can predict missing segments of code based on both preceding and succeeding context. This makes it exceptionally powerful for tasks like refactoring, bug fixing, and type inference where the surrounding logic is already defined. Built in 1.3B and 6.7B parameter variants, it was trained on a massive repository of 28+ programming languages from public GitHub repositories. In the 2026 market landscape, InCoder serves as a foundational architecture for specialized IDE plugins and automated refactoring agents that require more nuance than standard completion. While newer models have emerged, InCoder’s specific focus on the infilling objective remains a benchmark for technical tasks requiring middle-of-file synthesis without losing contextual coherence.
Uses a training objective where parts of the code are replaced by sentinel tokens and moved to the end of the sequence.
The multilingual AI assistant powered by Europe's premier frontier models.
The industry-standard framework for building context-aware, reasoning applications with Large Language Models.
Real-time, few-step image synthesis for high-throughput generative AI pipelines.
Professional-grade Generative AI for Landscape Architecture and Site Design.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Generates complete functions from natural language prompts without prior fine-tuning.
Employs unique tokens like <|file_separator|> and <|mask|> to define structural boundaries.
Trained on 159GB of code across 28+ languages including Python, JavaScript, C++, and Go.
Supports a context window of 2048 tokens, optimized for file-level comprehension.
Architecture optimized for translating descriptive comments into executable logic.
Capable of predicting types in dynamically typed languages based on usage context.
Developers often know where a bug is but struggle with the exact logic fix.
Registry Updated:2/7/2026
Verify with unit test
Converting old syntax to modern standards without breaking dependencies.
Undocumented codebases lead to high technical debt.