Starcoder2 3B - Details

Starcoder2 3B is a large language model developed by the Bigcodeproject, a non-profit organization. It features a 3b parameter size, offering robust performance across various tasks. The model is released under the BigCode Open Rail-M V1 License Agreement, ensuring transparency and accessibility. Its main focus lies in transparent training practices and providing three distinct sizes to cater to diverse needs.

Starcoder2 3B is a 3B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. It employs Grouped Query Attention and features a context window of 16,384 tokens, including a sliding window attention of 4,096 tokens. The model was trained using the Fill-in-the-Middle objective on 3+ trillion tokens, ensuring robust performance across diverse coding tasks. Its design emphasizes efficiency and scalability for complex programming challenges.

Starcoder2 3B has a 3b parameter size, placing it in the small model category, which ensures fast and resource-efficient performance for tasks requiring simplicity and speed. Its 16k context length allows handling extended sequences, making it suitable for complex coding tasks that demand long-text processing, though this requires more computational resources. The model’s design balances efficiency with capability, offering a practical solution for developers needing both scalability and performance.
- Parameter Size: 3b
- Context Length: 16k

Starcoder2 3B is a model with a 3b parameter size and a 16k context length, designed for tasks involving programming languages. Its possible uses include code generation, where it might assist in creating code snippets or entire programs, though this requires validation for accuracy and relevance. Possible applications also extend to code completion, where it could suggest lines of code based on context, but this needs thorough testing to ensure compatibility with specific programming environments. Potential use cases like code translation, converting code between languages, may offer value but would require evaluation for precision and edge cases. These possible uses highlight the model’s flexibility but emphasize the need for careful exploration to confirm effectiveness in real-world scenarios.
- code generation
- code completion
- code translation

Starcoder2 3B is a model with a 3b parameter size and a 16k context length, which could support possible applications in areas like code generation, where it might assist in drafting code snippets or entire programs, though this requires validation for accuracy. Potential uses could include code completion, where it might suggest context-aware lines of code, but this needs thorough testing for compatibility with specific programming environments. Possible applications might also involve code translation, converting code between languages, though precision and edge cases would require evaluation. Potential use cases could extend to collaborative coding tools, where it might aid in real-time suggestions or refactoring, but this would need rigorous assessment for reliability. Each of these possible applications highlights the model’s versatility but underscores the necessity of comprehensive evaluation and testing before deployment.
- code generation
- code completion
- code translation

Starcoder2 3B with the q4 quantized version requires a GPU with at least 12GB VRAM and 8GB–16GB VRAM for optimal performance, making it a possible choice for systems with mid-range graphics cards. This version balances precision and efficiency, though users should verify their hardware compatibility. Possible applications for this setup include code-related tasks, but thorough testing is recommended.
- fp16, q2, q3, q4, q5, q6, q8

Starcoder2 3B is a 3b parameter model developed by the non-profit Bigcodeproject, featuring a 16k context length and released under the BigCode Open Rail-M V1 License. It offers multiple quantized versions for varied hardware requirements, making it suitable for code-related tasks with potential applications that require thorough evaluation.

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