A high-performance merge of Phind and WizardCoder for state-of-the-art open-source code generation.
CodeBooga-34B-v0.1 is a sophisticated Large Language Model (LLM) architected as a merge between two industry-leading models: Phind-CodeLlama-34B-v2 and WizardCoder-Python-34B-V1.0. Developed by the creator of the Oobabooga Text-Generation-WebUI, this model specifically targets the intersection of reasoning depth and code accuracy. By leveraging the 34-billion parameter CodeLlama backbone, it provides a superior alternative to proprietary systems for enterprises requiring on-premise deployment. In the 2026 landscape, CodeBooga serves as a benchmark for 'local-first' development workflows, offering high-fidelity Python generation, complex algorithmic solving, and multilingual programming support. Its architecture is particularly optimized for FP16 and various quantization formats (GGUF, EXL2, AWQ), allowing it to run efficiently on prosumer hardware while maintaining a high HumanEval score. The model uses a refined instruction-following template, making it highly responsive to complex, multi-step engineering prompts without the latency associated with cloud-based API calls.
Combines Phind's reasoning with WizardCoder's instruction adherence using weight averaging.
Commercial-grade, open-source transformer architecture optimized for infinite context and enterprise scale.
A massively multilingual pre-trained text-to-text transformer covering 101 languages.
The industry-standard 7B parameter model outperforming models twice its size through efficiency.
A Generative Model for Code with Bidirectional Infilling and Program Synthesis capabilities.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Supports 4-bit, 5-bit, and 8-bit quantization with minimal perplexity loss.
Strict adherence to Instruction/Response formatting for predictable outputs.
Supports up to 16k context with RoPE scaling adjustments.
Specifically fine-tuned on vast repositories of Python libraries.
High performance in zero-shot chain-of-thought reasoning tasks.
Full local execution ensures zero data transit to external servers.
Developers spending 30% of time writing boilerplate tests.
Registry Updated:2/7/2026
Manually converting Python 2 to Python 3 or C++ to Rust.
Inability to use GitHub Copilot due to strict data privacy policies.