Update README.md

README.md

@@ -1,3 +1,103 @@
 ---
 license: cc-by-nc-4.0
+language:
+- en
 ---
+
+# 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 dataset in ChatML format, ensuring diverse and rich inputs.
+
+### Preprocessing
+Advanced preprocessing into ChatML format.
+
+### Training Hyperparameters and Fine-Tuning Details
+- Training Regime: FP32
+- Warmup Steps: 1
+- Per Device Train Batch Size: 2
+- Gradient Accumulation Steps: 64
+- Max Steps: 1500
+- Learning Rate: 0.00015
+- 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
+Represents a significant advancement in domain-specific language modeling, excelling in complex, authentic pirate-themed content.
+
+### 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.