Dolphin-2.9.2-qwen2-7b
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| Base Model | Qwen2-7B |
| License | Apache-2.0 |
| Context Length | 128k tokens |
| Training Length | 16k sequence length |
| Tensor Type | BF16 |
What is dolphin-2.9.2-qwen2-7b?
Dolphin-2.9.2-qwen2-7b is an advanced language model developed by Cognitive Computations, representing a significant evolution in uncensored AI capabilities. Built upon the Qwen2-7B architecture, this model has been carefully fine-tuned using 8 diverse datasets to enhance its performance across various tasks including instruction-following, conversation, and coding.
Implementation Details
The model implements full-weight fine-tuning with a 16k sequence length while maintaining the base model's impressive 128k context window. It utilizes BF16 tensor formatting and incorporates training from multiple high-quality datasets including OpenHermes-2.5, CodeFeedback, and specialized mathematics problem-solving datasets.
- Extensive dataset combination including Dolphin-2.9, OpenHermes-2.5, and specialized coding datasets
- Function calling support with dedicated training data
- Optimized for both conversation and technical tasks
- Uncensored architecture with minimal alignment constraints
Core Capabilities
- Advanced instruction following and conversational abilities
- Robust coding and technical problem-solving skills
- Function calling support for programmatic integration
- Initial agentic capabilities for autonomous task handling
- Extended context handling up to 128k tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its uncensored nature combined with extensive fine-tuning on diverse, high-quality datasets. It offers a rare combination of compliance and capability while maintaining ethical implementation flexibility for end users.
Q: What are the recommended use cases?
The model excels in conversational AI, coding assistance, technical documentation, and function calling implementations. However, users should implement their own alignment layer before deploying it in production services due to its uncensored nature.
