BioCoder is a challenging bioinformatics code generation benchmark for examining the capabilities of state-of-the-art large language models (LLMs).
الميزات الرئيسية لـ gersteinlab/biocoder هي: Benchmarks and Datasets.
تشمل البدائل مفتوحة المصدر لـ gersteinlab/biocoder: rllm-org/rllm — rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline… evalplus/evalplus — Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024. google-research/google-research — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum… hendrycks/apps — This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks\, Steven Basart\, Saurav… leolty/repobench — ✨ RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems - ICLR 2024. bytedance/fullstackbench — FullStack Bench: Evaluating LLMs as Full Stack Coders.
rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline that runs the same agent code for both evaluation and training, automatically capturing traces for gradient computation. The framework supports distributed reinforcement learning across multiple GPUs and nodes using pluggable backends, and executes agents in isolated sandboxes—either locally or in the cloud—for safe and scalable rollout collection. It trains agents built with LangGraph, SmolAgents, OpenAI Agents SDK, or custom frameworks without requiring core logic changes. T
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
FullStack Bench: Evaluating LLMs as Full Stack Coders