Dolphin Phi 2.7B
Dolphin Phi 2.7B is a large language model with 2.7 billion parameters developed by the community-driven maintainer Cognitive Computations. It operates under the Microsoft Research License Terms (MSRLT), allowing flexible use while adhering to specific guidelines. Designed as an uncensored model, it prioritizes user compliance and adaptability, making it suitable for diverse applications where direct interaction and customization are essential.
Description of Dolphin Phi 2.7B
Dolphin Phi 2.7B is a large language model based on Phi-2, trained using qLoRA and Axolotl frameworks with the ChatML prompt format. It operates under the MIT license, ensuring broad accessibility and flexibility. The model includes evaluation metrics across multiple benchmarks, providing transparency in performance. Designed as an uncensored model, it incorporates dataset filtering to remove alignment and bias, though additional alignment layers are required for safe deployment. Supported by Convai and enhanced by contributions from multiple researchers and frameworks, it emphasizes adaptability and user-driven customization.
Parameters & Context Length of Dolphin Phi 2.7B
Dolphin Phi 2.7B is a large language model with 2.7 billion parameters and a 4,000-token context length. The parameter size places it in the small to mid-scale range, offering efficient performance for tasks requiring moderate complexity while maintaining accessibility. Its context length supports short to moderate-length interactions but may limit handling of extended or highly detailed texts. The model’s design emphasizes adaptability and user-driven customization, balancing resource efficiency with functional flexibility.
- Name: Dolphin Phi 2.7B
- Parameter Size: 2.7b
- Context Length: 4k
- Implications: Efficient for simple tasks, limited context for long texts.
Possible Intended Uses of Dolphin Phi 2.7B
Dolphin Phi 2.7B is a large language model with 2.7 billion parameters and a 4,000-token context length, designed for text generation, code writing, and translation. These are possible applications that could benefit from its capabilities, though possible use cases may vary depending on specific requirements and constraints. The model’s uncensored nature and dataset filtering suggest it could support possible tasks requiring adaptability, but possible limitations in alignment or bias mitigation might necessitate further refinement. Possible users might explore its flexibility for creative or technical workflows, though possible outcomes would depend on implementation details and validation.
- Name: Dolphin Phi 2.7B
- Intended Uses: text generation, code writing, translation
- Purpose: adaptable, user-driven tasks
- Important Info: requires additional alignment layers for safe deployment
Possible Applications of Dolphin Phi 2.7B
Dolphin Phi 2.7B is a large language model with 2.7 billion parameters and a 4,000-token context length, offering possible applications in areas like text generation, code writing, and translation. These possible uses could leverage its adaptability and flexibility, though possible outcomes may vary depending on specific requirements and validation. Possible tasks might include generating creative content, assisting with programming tasks, or facilitating multilingual communication, but possible limitations in alignment or bias mitigation would require further investigation. Possible users could explore its potential for technical or creative workflows, though possible deployment scenarios would need thorough testing and refinement.
- Name: Dolphin Phi 2.7B
- Possible Applications: text generation, code writing, translation
- Important Info: requires additional alignment layers for safe deployment
Quantized Versions & Hardware Requirements of Dolphin Phi 2.7B
Dolphin Phi 2.7B with the q4 quantization offers a good balance between precision and performance, requiring a GPU with at least 8GB–16GB VRAM and a multi-core CPU. System memory should be at least 32GB for smooth operation. These are possible requirements, and users should verify compatibility with their hardware.
- Quantized Versions: q2, q3, q4, q5, q6, q8
- Name: Dolphin Phi 2.7B
- Important Info: Hardware needs vary by quantization level.
Conclusion
Dolphin Phi 2.7B is a 2.7 billion parameter large language model with a 4,000-token context length, designed for text generation, code writing, and translation. It operates under the MIT license, is uncensored with dataset filtering, and requires additional alignment layers for safe deployment, while offering quantized versions (q2, q3, q4, q5, q6, q8) to suit varying hardware capabilities.