phanerozoic/Mistral-Pirate-7b-v0.3

MistralPirate-7b-v0.3

Model Card

Description

MistralPirate-7b-v0.3 is a sophisticated language model fine-tuned for generating intricate and authentic pirate-themed content. This version, correcting our version control from v2 to v0.3, builds upon MistralPirate-7b-v2 and leverages advancements from Mistral Instruct v0.2. It shows improved performance in pirate dialect accuracy and perplexity scores.

• Developed by: phanerozoic
• License: cc-by-nc-4.0
• Finetuned from: Mistral Instruct v0.2

Version Control Correction

Correcting version control to v0.3 to reflect developmental progression and enhancements over the previous version.

Comparative Analysis with Previous Model

MistralPirate-7b-v0.3 demonstrates notable improvements over its predecessor in several key areas:

• Pirate Dialect: The new model uses richer and more immersive pirate vernacular, enhancing the thematic experience.
• Technical Accuracy: It shows a deeper understanding of complex sailing scenarios, providing detailed and practical advice in response to intricate questions.
• Language Coherence: The model maintains a consistent tone and style, effectively blending pirate jargon with technical expertise.

Direct Use

Ideal for interactive storytelling, gaming, advanced educational content, and conversational AI in pirate-themed settings.

Downstream Use

Suitable for tasks requiring detailed language generation and domain-specific knowledge, like advanced thematic content creation or immersive language learning.

Out-of-Scope Use

Not intended for general-purpose language modeling or non-pirate-themed contexts. Usage outside its specialization may result in suboptimal performance.

Bias, Risks, and Limitations

Limited by its training data, may inherit biases. Best used where pirate-themed language is appropriate, not for serious or sensitive communication.

Recommendations

Recommended for thematic contexts, with an understanding of its specialized focus. Not for accurate information outside pirate dialect specialization.

Custom Stopping Strings Usage

Custom stopping strings employed for output quality:

• "},"
• "User:"
• "You:"
• "\nUser"
• "\nUser:"

Training Data

Trained on a vast pirate themed dataset in ChatML format much larger than the previous version, ensuring diverse and rich inputs. This dataset was similarly derived from "Moby Dick".

Preprocessing

Advanced preprocessing into ChatML format.

Training Hyperparameters and Fine-Tuning Details

• Training Regime: FP32
• Warmup Steps: 1
• Per Device Train Batch Size: 1
• Gradient Accumulation Steps: 1
• Max Steps: 1500
• Learning Rate: 0.0002
• Logging Steps: 1
• Save Steps: 1
• Lora Alpha: 32
• Dimension Count: 16
• Specific Lora Fine-Tuning Run:
  • Step: 26
  • Loss: 1.4906
  • Learning Rate: 0.00019814951887490748
  • Epoch: 0.01

Speeds, Sizes, Times

Approximately 12 minutes training time on RTX 6000 Ada GPU.

Testing Data

Achieved a perplexity score of 5.17 against the Wikitext database.

Factors

Focus on language coherence, pirate dialect adherence, and technical accuracy.

Metrics

Primary metric: Perplexity. Qualitative assessments of dialect authenticity and technical content.

Results

Marked improvement in sophisticated output with authentic pirate tone. Lower perplexity score demonstrates enhanced language modeling.

Summary

MistralPirate-7b-v0.3 represents a significant leap in domain-specific language modeling, particularly in the realm of pirate-themed content generation. This version not only corrects the version control nomenclature but also marks a substantial advancement in the model's capabilities. It blends the charm and authenticity of pirate vernacular with the precision of modern language modeling techniques.

The model has been meticulously fine-tuned to capture the nuances of pirate dialect while maintaining a high degree of language coherence and technical accuracy. This makes it an unparalleled tool in scenarios where an immersive pirate theme is desired, be it in storytelling, gaming, or educational settings. The enhanced perplexity scores and technical refinements reflect its ability to handle complex, multi-faceted queries with a flair unique to pirate lore.

Designed to navigate the challenging waters of thematic content generation, MistralPirate-7b-v0.3 stands as a testament to the possibilities of domain-specific language models. It showcases the seamless integration of thematic accuracy with advanced AI capabilities, ensuring that users can enjoy an authentic and engaging pirate experience.

Model Architecture and Objective

Based on Mistral Instruct v0.2, fine-tuned for high coherence and technical accuracy in pirate-themed content.

Compute Infrastructure

Trained on RTX 6000 Ada GPU for efficient training and improved perplexity scores.

Hardware

• Type: RTX 6000 Ada
• Utilization: Approx. 12 minutes for training.

Acknowledgments

Gratitude to Mistral and Mistral Instruct v0.2 teams. Appreciation to the language modeling community for support in domain-specific model enhancement.