mlsanityData to 7 Oct 2026

Granite Code Models: A Family of Open Foundation Models for Code Intelligence

arXiv 2405.04324 ↗First posted 7 May 2024cs.AI cs.CL cs.SEcited by 116

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Abstract (arXiv)

Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). Builds on 43 of its 59 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

43 of 59 references.

References · 43

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31 more
  1. 2019Root Mean Square Layer Normalizationcited by 1,425
  2. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  3. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  4. 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,311
  5. 2017Mixed Precision Trainingcited by 1,135
  6. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 27; 2025: 58; 2026: 31.

116 papers.

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  1. 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 527
  2. 2024OpenCoder: The Open Cookbook for Top-Tier Code Large Language Modelscited by 127
  3. 2024Large Language Model Unlearning via Embedding-Corrupted Promptscited by 112
  4. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
  5. 2025HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languagescited by 71
  6. 2024Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Taskscited by 52
  7. 2024NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Callscited by 45
  8. 2025Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gapcited by 41
  9. 2024Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMscited by 39
  10. 2025Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLcited by 38
  11. 2024ACPBench: Reasoning about Action, Change, and Planningcited by 37
  12. 2024Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Modelscited by 29
38 more
  1. 2024Foundation Models for the Electric Power Gridcited by 27
  2. 2025AutoPDL: Automatic Prompt Optimization for LLM Agentscited by 27
  3. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  4. 2024On Mitigating Code LLM Hallucinations with API Documentationcited by 21
  5. 2026Composer 2 Technical Reportcited by 20
  6. 2024ExecRepoBench: Multi-level Executable Code Completion Evaluationcited by 19
  7. 2024Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generationcited by 16
  8. 2024Language model developers should report train-test overlapcited by 16
  9. 2025HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Taskscited by 16
  10. 2025DependEval: Benchmarking LLMs for Repository Dependency Understandingcited by 16
  11. 2024Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucinationcited by 14
  12. 2025LLM Strategic Reasoning: Agentic Study through Behavioral Game Theorycited by 14
  13. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  14. 2025Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metricscited by 12
  15. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  16. 2025Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deploymentcited by 10
  17. 2025FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Datacited by 10
  18. 2025MTIR-SQL: Multi-turn Tool-Integrated Reasoning Reinforcement Learning for Text-to-SQLcited by 10
  19. 2024Turning Generative Models Degenerate: The Power of Data Poisoning Attackscited by 9
  20. 2024Scaling Granite Code Models to 128K Contextcited by 9
  21. 2025Knowledge Base Construction for Knowledge-Augmented Text-to-SQLcited by 9
  22. 2025textttDroid: A Resource Suite for AI-Generated Code Detectioncited by 9
  23. 2024Evaluation of Code LLMs on Geospatial Code Generationcited by 8
  24. 2025Towards Formal Verification of LLM-Generated Code from Natural Language Promptscited by 8
  25. 2026ShieldedCode: Learning Robust Representations for Virtual Machine Protected Codecited by 8
  26. 2025Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?cited by 7
  27. 2025Granite Embedding R2 Modelscited by 7
  28. 2026M2RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modelingcited by 7
  29. 2024XMainframe: A Large Language Model for Mainframe Modernizationcited by 6
  30. 2025A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Modelscited by 6
  31. 2025ACPBench Hard: Unrestrained Reasoning about Action, Change, and Planningcited by 6
  32. 2025The Consistency Hypothesis in Uncertainty Quantification for Large Language Modelscited by 6
  33. 2026SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inferencecited by 6
  34. 2024Arctic-SnowCoder: Demystifying High-Quality Data in Code Pretrainingcited by 5
  35. 2024ETF: An Entity Tracing Framework for Hallucination Detection in Code Summariescited by 5
  36. 2025TRACE: Transparent Web Reliability Assessment with Contextual Explanationscited by 5
  37. 2025Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarizationcited by 5
  38. 2024Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generationcited by 4

50 most cited of 116 All 116 (JSON)

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  1. 2025Towards Advancing Code Generation with Large Language Models: A Research Roadmap
  2. 2025Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency
  3. 2025ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
  4. 2025A Survey on Code Generation with LLM-based Agents
  5. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
  6. 2025Seed-Coder: Let the Code Model Curate Data for Itself
  7. 2024Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs
  8. 2024CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs