# Tiny-Pirate-1.1b-v0.1-GGUF-fp16

Tiny-Pirate-1.1b-v0.1-GGUF-fp16 is a compact and specialized language model designed for generating authentic pirate-themed content, optimized in the GGUF file format in fp16 precision. This model is fine-tuned from the TinyLlama-1.1B model and is specifically adapted to operate efficiently in CPU-only and resource-limited environments.

- **Developed by**: phanerozoic
- **License**: cc-by-nc-4.0
- **Finetuned from**: TinyLlama-1.1B

### Performance
The Tiny-Pirate-1.1B model exhibits a robust ability to generate pirate-themed content, demonstrating a strong grasp of pirate vernacular and thematic elements. The responses are notably coherent and contextually appropriate, reflecting the model's adeptness at maintaining a consistent pirate tone.

### Direct Use
Ideal for applications requiring thematic language generation in resource-constrained environments, such as edge computing, mobile devices, and lightweight AI applications.

### Training Data
Utilized the same pirate-themed dataset as MistralPirate-7b-v0.3, ensuring rich and diverse inputs for fine-tuning.

### Custom Stopping Strings
To enhance output quality, the following custom stopping strings were employed:
- "},"
- "User:"
- "You:"
- "\nUser"
- "\nUser:"
- "me:"
- ""\n"

### Training Hyperparameters and Fine-Tuning Details
- **LoRA Rank**: 16
- **LoRA Alpha**: 32
- **True Batch Size**: 4
- **Gradient Accumulation Steps**: 1
- **Epochs**: 1
- **Learning Rate**: 3e-4
- **LR Scheduler**: Linear
- **LLaMA Target Projections**: All targets modified
- **Fine-Tuning Approach**: LoRA peft merged back into the base model

### Limitations
While adept at generating pirate-themed content, Tiny-Pirate-v0.1 may not handle highly complex language tasks as larger models do. Its specialization in pirate dialect limits its use in general language applications.

### Compute Infrastructure
Efficiently trained on an RTX 6000 Ada GPU, taking approximately 2-3 minutes, showcasing resource-effective training for specialized models.

### Results
The model successfully produced responses that are thematically aligned with typical pirate lore and language. The outputs are engaging and largely relevant to the queries, showcasing the model's capacity to handle a variety of pirate-related topics from navigation to mythology. 

### Summary
Tiny-Pirate-1.1B stands out as an effective tool for generating pirate-themed content, particularly suitable for applications where thematic consistency and lighter computational demands are key. 

### Acknowledgments
Gratitude is extended to the developers of TinyLlama-1.1B for their foundational work, which was instrumental in the creation of Tiny-Pirate-v0.1.
{"language": ["en"], "license": "cc-by-nc-4.0", "widget": [{"text": "User: How can I make my daily meals healthier?\nAssistant:\n", "example_title": "Healthy Eating Tips"}, {"text": "User: What's a good hobby to start for creative expression?\nAssistant:\n", "example_title": "Hobby Suggestions for Creativity"}, {"text": "User: How can I reduce stress after a long day at work?\nAssistant:\n", "example_title": "Stress Reduction Strategies"}, {"text": "User: What are some effective ways to save money?\nAssistant:\n", "example_title": "Money-Saving Tips"}, {"text": "User: What's the best approach to learn a new language quickly?\nAssistant:\n", "example_title": "Language Learning Techniques"}]}
transformers gguf llama en license:cc-by-nc-4.0 endpoints_compatible text-generation-inference region:us