dphn/dolphin-2.8-experiment26-7b
dphn/dolphin-2.8-experiment26-7b is a 7 billion parameter language model based on Yam Peleg's Experiment-26-7B, featuring a 4096 token context length. This Dolphin variant is specifically fine-tuned with extensive coding data, making it highly proficient in programming tasks. It is designed for developers seeking a capable model for code generation and related applications.
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Dolphin 2.8 Experiment26 7b Overview
dphn/dolphin-2.8-experiment26-7b is a 7 billion parameter language model, fine-tuned from Experiment-26 by Yam Peleg. This iteration of Dolphin is notably optimized for coding tasks, having been trained with a significant volume of coding-specific data. The base model supports a 16k context, though this specific variant is listed with a 4096 token context length.
Key Training Details
- Training Method: Fine-tuned using QLoRA on Axolotl over 3 epochs.
- Prompt Format: Utilizes the ChatML prompt format, with a specific system prompt example provided for uncensored and unbiased AI assistance.
Intended Use Cases
- Coding: Excels particularly in coding-related applications due to its specialized training data.
- General Chat: While focused on coding, future plans for Dolphin 3.0 include enhancements for general chat, structured output, agent cases, and role-playing.
This model was developed with sponsorship from MassedCompute and acknowledges contributions from various open-source community members and dataset authors.