This repository houses the OpenOrca-Platypus2-13B-PirateTalk model, a variant of the OpenOrca-Platypus2 13b (16 float) model, which has been specifically fine-tuned on a diverse dataset of pirate-centric content. The content spans from typical pirate phrases and extended conversation fragments to the more niche and arcane aspects of pirate vernacular.

Objective: Our primary goal in developing OpenOrca-Platypus2-13B-PirateTalk was to discern if we could achieve a specific dialect and diction enforcement through fine-tuning. The aspiration was to instruct the model to produce a language reminiscent of authentic pirate parlance, in both vocabulary and syntactic structure.

Evolution: This model embodies our second venture in refining OpenOrca-Platypus2 with a pirate twist. Unlike the prior iteration that relied on an external LoRA adapter, OpenOrca-Platypus2-13B-PirateTalk integrates the fine-tuning directly, eliminating the need for external adapter manipulation. This integrated approach, combined with a richer dataset that ranges from succinct phrases to detailed samples, seeks to push the model's adaptability boundaries.

Outcomes: By leveraging a more comprehensive dataset and merging the fine-tuning directly into the model, OpenOrca-Platypus2-13B-PirateTalk has shown improved inference quality compared to the previous setup with a separate adapter. The model more naturally grasps the subtleties and idiosyncrasies of pirate dialect, thereby providing users with a genuinely immersive pirate-speak experience.

Quantized Performance: An intriguing observation we made post-merging was the model's passable performance in its quantized state. Preliminary analyses suggest that this acceptable performance may be attributed more to the merging of the LoRA adapter into the base model rather than the inherent strength or specificity of the pirate dataset itself. While quantized models often face challenges in retaining the finesse of their full-precision counterparts, the integration of the adapter appears to have endowed OpenOrca-Platypus2-13B-PirateTalk with a resilience that allows it to handle quantization reasonably well. Users should still approach the quantized version with tempered expectations, but its performance is commendably decent given the typical trade-offs involved.

Note: While users may occasionally notice the model generating lengthy streams of text, it's important to understand that this behavior isn't particular to the OpenOrca-Platypus2-13B-PirateTalk variant alone but is a trait seen in the foundational OpenOrca-Platypus2 model as well.
{"license": "cc-by-nc-4.0"}
transformers pytorch llama text-generation license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us