Granite Code 8B Base - Details

Granite Code 8B Base, developed by IBM Granite, is a large language model with 8 billion parameters. It is released under the Apache License 2.0, making it accessible for both research and commercial use. Designed as part of the Granite Code family, this model focuses on generative tasks, particularly in code-related applications.

Granite Code 8B Base, developed by IBM Granite, is a decoder-only code model designed for generative tasks such as code generation, code explanation, and code fixing. Trained from scratch using a two-phase strategy, it first processed 4 trillion tokens across 116 programming languages, then refined its capabilities with 500 billion tokens combining code and natural language data to improve reasoning and instruction-following. This model is released under the Apache License 2.0, enabling flexible use for research and commercial applications. Its architecture focuses on enhancing code-related tasks through extensive and diverse training data.

Granite Code 8B Base, with 8 billion parameters, falls into the mid-scale category of large language models, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 4,096-token context length places it in the short-context range, making it well-suited for tasks requiring focused, concise input but less effective for handling very long texts. The parameter size enables efficient deployment on standard hardware, while the context length limits its ability to process extended sequences, such as lengthy codebases or detailed documents.

• Parameter Size: 8b
• Context Length: 4k
• Implications: Mid-scale performance for moderate tasks, short-context suitability with limitations for long texts.

Granite Code 8B Base, a large language model designed for code-related generative tasks, has possible applications in areas such as code generation, code explanation, and code fixing. Its possible use in generating unit tests or documentation could streamline development workflows, while possible roles in addressing technical debt or vulnerability detection might aid in improving code quality. The model’s possible ability to assist with code translation could support cross-language project transitions. However, these possible uses require thorough investigation to ensure effectiveness and alignment with specific requirements. The model’s focus on code tasks makes it a potential tool for developers seeking automation or assistance in software-related activities.

• code generation
• code explanation
• code fixing
• generating unit tests
• generating documentation
• addressing technical debt issues
• vulnerability detection
• code translation

Granite Code 8B Base, a large language model focused on code-related tasks, has possible applications in areas such as code generation, where it could assist in drafting code snippets or entire functions. It also has possible utility in code explanation, offering insights into complex code structures or algorithms. Possible uses include generating unit tests to automate testing processes or translating code between programming languages to support cross-platform development. These possible applications, while promising, require thorough evaluation to ensure alignment with specific project needs and technical constraints.

• code generation
• code explanation
• generating unit tests
• code translation

Granite Code 8B Base, with its medium q4 quantization, requires a GPU with at least 16GB VRAM and a system with 12GB–24GB VRAM to balance precision and performance efficiently. This version is optimized for deployment on mid-range hardware, making it accessible for developers seeking a practical trade-off between speed and accuracy. The model’s 8B parameters align with the hardware requirements for 8B-scale models, ensuring compatibility with standard GPUs while maintaining reasonable computational efficiency.

• fp16, q2, q3, q4, q5, q6, q8

Granite Code 8B Base, developed by IBM Granite, is a large language model with 8 billion parameters, released under the Apache License 2.0, designed for code-related generative tasks. It undergoes a two-phase training process, first on 4 trillion tokens across 116 programming languages and then on 500 billion tokens combining code and natural language data, with a context length of 4,096 tokens.

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