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Model: phanerozoic/Mistral-Pirate-7b-v2
Source: Original Platform

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README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+---
+
+![mistralpirate2.jpg](https://huggingface.co/phanerozoic/MistralPirate-7b-v2/resolve/main/mistralpirate2.jpg)
+
+
+# MistralPirate-7b-v2
+
+This model card describes MistralPirate-7b-v2, an advanced language model specifically fine-tuned for generating coherent and accurate pirate-themed content. This model represents a significant improvement over its predecessor, leveraging the OpenHermes 2.5 base model and a substantially expanded and structured dataset.
+
+### Model Description
+
+- **Developed by:** phanerozoic
+- **License:** cc-by-nc-4.0
+- **Finetuned from model:** OpenHermes 2.5
+
+### Direct Use
+
+MistralPirate-7b-v2 excels in generating pirate dialect and is ideal for applications in interactive storytelling, gaming, educational content, and conversational AI where pirate-themed language is desired.
+
+### Downstream Use
+
+The model can be adapted for various downstream tasks that require a blend of creative language generation and domain-specific knowledge, such as in thematic content creation or language learning tools.
+
+### Out-of-Scope Use
+
+MistralPirate-7b-v2 is not designed for general-purpose language modeling or contexts outside of its pirate-themed training. Using it in non-pirate or more formal applications may result in suboptimal performance.
+
+## Bias, Risks, and Limitations
+
+MistralPirate-7b-v2, while exhibiting improved coherence and factual accuracy, is still limited by its training data and may inherit biases present within. It is best used in contexts where pirate-themed language is appropriate and not for serious or sensitive communication.
+
+### Recommendations
+
+Users should be aware of the model's thematic focus and limitations. It is recommended to use MistralPirate-7b-v2 in appropriate thematic contexts and avoid relying on it for accurate information outside its pirate dialect expertise.
+
+## Custom Stopping Strings Usage
+
+To enhance the output quality and coherence, MistralPirate-7b-v2 is configured to recognize certain custom stopping strings. These strings are:
+
+- "},"
+- "User:"
+- "You:"
+- "\nUser"
+- "\nUser:"
+
+These stopping strings are crucial in guiding the model to accurately determine the end of a response or a segment in conversation. Their usage is particularly effective in scenarios involving dialogue, helping to maintain clarity and context in the model's outputs.
+
+### Training Data
+
+The model was trained on a dataset that is ten times larger than its predecessor's, composed of pirate-themed content formatted in ChatML.
+
+#### Preprocessing
+
+The training data, unlike the data from v1, was preprocessed into ChatML format to provide structured and complex training input.
+
+#### Training Hyperparameters
+
+- **Training Regime:** FP32
+- **Warmup Steps:** 1
+- **Per Device Train Batch Size:** 1
+- **Gradient Accumulation Steps:** 64
+- **Max Steps:** 1000
+- **Learning Rate:** 0.0002
+- **Logging Steps:** 1
+- **Save Steps:** 1
+- **Lora Alpha:** 32
+- **Dimension Count:** 16
+
+#### Speeds, Sizes, Times
+
+- Training was completed in approximately 10 minutes on an RTX 6000 Ada GPU.
+
+#### Testing Data
+
+The model was evaluated against the Wikitext database, achieving a perplexity score of 5.65.
+
+#### Factors
+
+Evaluation focused on language coherence and adherence to the pirate dialect.
+
+#### Metrics
+
+Perplexity was used as the primary metric to assess the model's language modeling performance.
+
+### Results
+
+The model demonstrated a significant improvement in language coherence and factual accuracy compared to its predecessor.
+
+## Performance Highlights
+
+MistralPirate-7b-v2 has shown a marked improvement in producing rigorous and sensible outputs while maintaining a pirate tone. Unlike its predecessor, this version consistently maintains coherence in its language generation, veering away from nonsensical responses. A significant achievement is its perplexity score against the Wikitext database, which stands at about 5.65, demonstrating its enhanced language modeling capabilities.
+
+#### Summary
+
+MistralPirate-7b-v2 marks a notable advancement in domain-specific language modeling, particularly in generating pirate-themed content.
+
+### Model Architecture and Objective
+
+MistralPirate-7b-v2 is based on the OpenHermes 2.5 architecture, fine-tuned to generate pirate-themed content with high coherence and factual accuracy.
+
+### Compute Infrastructure
+
+The model was trained on an RTX 6000 Ada GPU, with a focus on rapid and efficient training.
+
+#### Hardware
+
+- **Type:** RTX 6000 Ada
+- **Utilization:** Used for a total duration of approximately 10 minutes for the complete training process.
+
+## Acknowledgments
+
+We extend our deepest gratitude to the teams behind the Mistral and OpenHermes 2.5 models. Their groundbreaking work in language modeling provided the foundation upon which MistralPirate-7b-v2 was developed. Special thanks to the OpenHermes team for their contributions and support in advancing the capabilities of domain-specific language models.

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special_tokens_map.json

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+{
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+    "<|im_end|>",
+    "<|im_start|>"
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+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+}