
Codeium
A high-performance AI coding assistant optimized for deep repository awareness.

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.
CodeBooga-34B-v0.
Explore all tools that specialize in generate code snippets. This domain focus ensures CodeBooga-34B-v0.1 delivers optimized results for this specific requirement.
Explore all tools that specialize in generate python code. This domain focus ensures CodeBooga-34B-v0.1 delivers optimized results for this specific requirement.
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Combines Phind's reasoning with WizardCoder's instruction adherence using weight averaging.
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.
Provision a Linux environment with at least 24GB of VRAM (RTX 3090/4090).
Install an inference engine such as llama.cpp, ExLlamaV2, or vLLM.
Download the CodeBooga-34B-v0.1 weights from Hugging Face.
Select the appropriate quantization format (e.g., Q4_K_M for GGUF) based on memory constraints.
Configure the prompt template to use the 'Alpaca' or 'CodeLlama' instruction format.
Initialize the local API server using the --api flag in Text-Generation-WebUI.
Connect your IDE (VS Code/Cursor) via an LLM proxy or extension.
Set temperature to 0.1 for deterministic coding tasks or 0.7 for creative problem solving.
Benchmark local inference speed to ensure at least 10-15 tokens/sec.
Integrate with local CI/CD pipelines for automated code reviews.
All Set
Ready to go
Verified feedback from other users.
"Users highly value CodeBooga for its ability to follow complex logic where smaller models fail, particularly in Python development. Its open-source nature makes it a favorite for privacy-conscious engineers."
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