Training Data For Starcoder2 And Code Generation Models

67 TB permissively licensed code dataset across 600+ languages.

Unique: Curated and published as the official training dataset for StarCoder2 models, providing permissively-licensed, deduplicated, PII-removed code across 600+ languages with repository context and governance

vs others: More comprehensive and higher-quality than previous code datasets (CodeSearchNet, GitHub-Code) with rigorous deduplication, PII removal, and licensing compliance; enables training of state-of-the-art code models

via “open-source code generation model”

Open code model trained on 600+ languages.

Unique: StarCoder2 stands out due to its extensive training on The Stack v2 dataset and support for a wide range of programming languages.

vs others: Compared to alternatives, StarCoder2 offers superior context length and multi-language capabilities, making it ideal for diverse coding tasks.

via “code-generation-and-completion”

Mistral's mixture-of-experts model with efficient routing.

Unique: Explicitly documented as having 'strong performance' on code generation tasks with HumanEval benchmark results, achieved through training on code-inclusive datasets and instruction-tuning via SFT + DPO. Sparse routing architecture enables code generation at 6x faster inference speed than dense 70B models.

vs others: Provides open-source code generation with GPT-3.5-level performance and 6x faster inference than Llama 2 70B, enabling self-hosted code completion without reliance on proprietary APIs or external services.

via “competitive coding task performance with transformer architecture”

01.AI's bilingual 34B model with 200K context option.

Unique: Achieves competitive coding performance through general-purpose transformer pretraining on 3 trillion tokens without documented code-specific fine-tuning or instruction tuning, suggesting strong code representation learning from raw pretraining data. Bilingual training enables code generation with Chinese comments and documentation.

vs others: Provides competitive coding capability at 34B scale without the specialized training overhead of CodeLlama or Codex, reducing model size and inference cost while maintaining reasonable code quality for non-critical applications.

via “code generation with mathematical and logical reasoning”

Alibaba's code-specialized model matching GPT-4o on coding.

Unique: Trained on 5.5 trillion tokens including mathematical content, enabling integrated code generation and mathematical reasoning without separate modules — most code models lack explicit mathematical training, requiring prompting tricks or external math libraries

vs others: Combines code generation with mathematical reasoning in a single model, reducing latency and complexity vs. pipeline approaches using separate code and math models

via “code generation and programming task completion”

TII's 180B model trained on curated RefinedWeb data.

Unique: Leverages 180B parameters and 3.5T diverse training tokens to support code generation across multiple languages without language-specific fine-tuning, enabling emergent cross-language understanding and translation capabilities, though without specialized code-focused datasets like CodeSearchNet or GitHub.

vs others: Larger parameter count than Codex-based models enables better multi-language support and reasoning about code logic, but lacks specialized code training data and real-time IDE integration compared to GitHub Copilot, and requires local GPU infrastructure instead of cloud API access.

via “code generation and understanding with syntax-aware completion”

Shanghai AI Lab's multilingual foundation model.

Unique: Trained on diverse code corpora with syntax-aware tokenization that preserves indentation and bracket structure, enabling better code generation than models using generic tokenizers; InternLM2.5 adds improved reasoning for complex algorithmic problems

vs others: Comparable code generation to Codex/GPT-4 on standard benchmarks while being fully open-source and deployable locally; stronger than Llama 2 on code tasks due to more extensive code-specific instruction tuning

via “instruction-following code generation with fine-tuned response formatting”

DeepSeek's 236B MoE model specialized for code.

Unique: Instruction-tuned variants (Instruct models) are fine-tuned on instruction-response pairs to follow user specifications precisely, while maintaining the sparse MoE architecture and 128K context of base models

vs others: Provides instruction-following capabilities comparable to GPT-4-Turbo while remaining open-source and deployable locally, with explicit control over fine-tuning data vs proprietary models

via “code generation and completion with 88.4% humaneval performance”

Meta's 70B open model matching 405B-class performance.

Unique: Achieves 88.4% HumanEval pass rate at 70B parameters through instruction-tuning and code-specific training data, matching or exceeding many larger closed-source models while remaining open-weight and self-hostable

vs others: Outperforms GitHub Copilot (which uses Codex/GPT-4 variants) on HumanEval benchmarks while offering full model transparency and self-hosted deployment without API dependencies

via “code generation and technical reasoning”

text-generation model by undefined. 36,85,809 downloads.

Unique: Instruction-tuned on diverse code datasets including problem-solving patterns, algorithm design, and debugging tasks. Uses causal attention to maintain code structure and indentation, and supports few-shot learning through in-context examples without requiring fine-tuning or external retrieval systems.

vs others: More capable than CodeLlama-3.2-3B on instruction-following code tasks due to broader instruction-tuning; smaller and faster than CodeLlama-34B while maintaining acceptable code quality for single-file generation, making it suitable for resource-constrained environments.

via “code generation and understanding across multiple programming languages”

text-generation model by undefined. 47,03,591 downloads.

Unique: Trained on CodeFeedback-Filtered-Instruction (human-curated code quality feedback) and dolphin-coder datasets, enabling the model to generate not just syntactically valid code but code that follows best practices and idioms, rather than generic token-matching approaches used in simpler code completion models

vs others: Generates more idiomatic and maintainable code than base language models due to CodeFeedback training, while remaining fully open-source and deployable locally unlike Copilot; smaller than Codex-scale models but with better instruction-following for code generation tasks

via “encoder-decoder code generation with instruction tuning”

Home of CodeT5: Open Code LLMs for Code Understanding and Generation

Unique: Uses instruction-tuning objectives on top of T5 encoder-decoder architecture specifically for code, enabling natural language-guided generation with structured programming constraints rather than generic seq2seq prediction

vs others: Outperforms GPT-3.5 on instruction-following code tasks (36.1% vs ~25% Pass@1) while being fully open-source and fine-tunable, unlike proprietary models

via “code generation and explanation with instruction-following”

This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus). The model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-...

Unique: Fine-tuned on Claude's code generation outputs, capturing Anthropic's approach to code explanation and safety considerations (e.g., error handling suggestions) rather than pure code-to-code translation

vs others: Provides better code explanations and safety context than specialized code models like CodeLlama, but likely slower and less specialized than models fine-tuned specifically on code-only datasets

via “code-generation-and-refactoring”

Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either...

Unique: 70B parameter scale enables context-aware code generation that tracks variable types and function signatures across 4K+ token contexts, whereas smaller models lose type information after ~1K tokens

vs others: Comparable to Copilot for single-file generation but stronger at multi-file refactoring due to larger context window; more cost-effective than Claude for routine code tasks

via “code generation and completion with language-specific patterns”

GLM 4 32B is a cost-effective foundation language model. It can efficiently perform complex tasks and has significantly enhanced capabilities in tool use, online search, and code-related intelligent tasks. It...

Unique: GLM 4 32B includes specialized training on code-related tasks with enhanced support for tool-use patterns, making it particularly effective at generating code that calls APIs or external functions — not just standalone code

vs others: More cost-effective than Copilot Pro or Claude for code generation while maintaining competitive accuracy on tool-use and API integration patterns due to specialized training

via “code generation and technical problem-solving”

gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...

Unique: Trained on diverse code repositories with MoE routing that specializes expert networks for different programming paradigms (functional, OOP, procedural); enables language-agnostic code understanding and cross-language pattern transfer

vs others: More cost-effective than GitHub Copilot for batch code generation; comparable code quality to GPT-4 for most languages while maintaining lower latency through sparse activation

via “code understanding and generation”

Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context...

Unique: Code-optimized tokenizer and training corpus enable efficient code understanding without language-specific routing, with SSM architecture providing linear-complexity processing for long code files

vs others: Comparable code quality to GitHub Copilot and Claude 3.5 for generation, with better latency for long files due to SSM architecture; less specialized than Codex but more efficient

via “code generation and technical explanation”

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models. It is...

Unique: Instruction-tuned specifically for code tasks through Wizard training methodology, enabling it to generate not just functional code but well-documented, idiomatic implementations with explicit reasoning about design choices; mixture-of-experts routing allows specialized handling of different programming paradigms

vs others: Produces more readable and documented code than base models while maintaining competitive quality with specialized code models like Codex, with the advantage of being openly available and not restricted to specific languages or frameworks

via “code generation and technical explanation with multi-language support”

Trinity-Large-Preview is a frontier-scale open-weight language model from Arcee, built as a 400B-parameter sparse Mixture-of-Experts with 13B active parameters per token using 4-of-256 expert routing. It excels in creative writing,...

Unique: Multi-language code generation trained on diverse repositories with sparse MoE architecture potentially enabling language-specific expert routing (Python experts, JavaScript experts, etc.) for optimized code generation per language, though routing is opaque to users

vs others: Open-weight model allows fine-tuning for domain-specific code patterns unlike Copilot, and sparse routing enables faster inference for code completion workflows compared to dense 400B alternatives

via “code generation and completion with multi-language support”

The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to April 2023.

Unique: Trained on a curated, high-quality subset of public code repositories with deduplication and filtering for correctness, rather than all available code. This results in better adherence to best practices and fewer security anti-patterns compared to models trained on raw GitHub data.

vs others: Outperforms GitHub Copilot on code generation from natural language descriptions due to larger model size and instruction-following training; comparable to Claude 3 Opus on code quality but faster inference due to optimized architecture.