Dolphincoder 7B - Details

Dolphincoder 7B is a large language model developed by the community-driven maintainer Cognitive Computations. With a parameter size of 7b, it is designed to excel in coding tasks, particularly within the Uncensored Dolphin models series. The model operates under the BigCode Open Rail-M V1 License Agreement, ensuring open access and collaboration. Its focus on coding capabilities makes it a valuable tool for developers and researchers seeking efficient code generation and problem-solving.

Dolphincoder 7B is a large language model based on StarCoder2-7b designed for coding tasks with a focus on extensive coding data training. It operates as an uncensored model while ensuring dataset compliance through filtering. The model requires an additional alignment layer to address ethical considerations and promote responsible use. Its foundation in StarCoder2-7b enhances its capabilities in code generation and problem-solving.

Dolphincoder 7B features a 7b parameter size, placing it in the mid-scale category of open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 16k context length falls into the long context range, enabling it to handle extended texts and complex sequences while requiring more computational resources. This combination makes it suitable for coding tasks demanding both depth and breadth of understanding.
- Parameter Size: 7b
- Context Length: 16k

Dolphincoder 7B is a large language model designed for coding tasks, with possible applications in writing code in various programming languages, debugging and troubleshooting code, and generating code snippets for common tasks. Its possible use cases include assisting developers in rapid prototyping, automating repetitive coding workflows, or providing explanations for complex code structures. However, these possible applications require thorough investigation to ensure alignment with specific project needs and ethical guidelines. The model’s focus on coding makes it a possible tool for educational purposes, collaborative development, or enhancing productivity in software-related workflows. Further exploration is needed to fully understand its capabilities and limitations in these areas.
- writing code in various programming languages
- debugging and troubleshooting code
- generating code snippets for common tasks

Dolphincoder 7B is a large language model with possible applications in coding-related tasks, such as generating code in multiple programming languages, debugging and troubleshooting existing code, creating code snippets for routine tasks, and assisting with code explanations. These possible uses could support developers in accelerating workflows, improving code quality, or learning programming concepts. However, the possible effectiveness of these applications depends on specific contexts and requires thorough evaluation to ensure alignment with user needs. The model’s focus on coding makes it a possible tool for educational settings, collaborative projects, or automation of repetitive coding steps. Each possible application must be carefully assessed and tested before deployment to ensure reliability and suitability.
- writing code in various programming languages
- debugging and troubleshooting code
- generating code snippets for common tasks
- assisting with code explanations and automation

Dolphincoder 7B in its medium q4 version is optimized for a balance between precision and performance, requiring a GPU with at least 16GB VRAM and a system with 32GB RAM for smooth operation. This configuration allows it to run efficiently on mid-range hardware, though higher VRAM capacity may be needed for complex tasks. Possible applications of this version include coding assistance and code generation, but users should verify their hardware compatibility.
- fp16, q2, q3, q4, q5, q6, q8

Dolphincoder 7B is a large language model based on StarCoder2-7b, optimized for coding tasks with a 7b parameter size and 16k context length, designed to generate code, debug, and assist with programming challenges. It operates under an open license, requires an alignment layer for ethical use, and is tailored for developers seeking efficient code solutions.

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