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phanerozoic/Mistral-Cowboy-7b-v0.1

sourceHugging Facecc-by-nc-4.0updated 3y agoView on Hugging Face
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Model Card

mistralcowboy.png

MistralCowboy-7b-v0.1

This model card introduces MistralCowboy-7b-v0.1, a state-of-the-art language model created by Phanerozoic. It stands as our finest achievement, expertly fine-tuned to emulate the distinctive style and lingo of a classic cowboy. This model excels in bringing the unique cowboy vernacular and worldview to life in engaging and entertaining conversations.

Model Description

  • —Developer: Phanerozoic
  • —License: cc-by-nc-4.0
  • —Finetuned from: OpenHermes 2.5
  • —Primary Use: Ideal for applications that require dialogues in the distinct cowboy style, such as interactive storytelling, virtual role-playing games, and educational platforms exploring historical and cultural aspects of the American West.

Direct Use

MistralCowboy-7b-v0.1 is intended for immersive experiences where users interact with a virtual cowboy, gaining insights into the lifestyle, language, and ethos of the old West.

Downstream Use

While primarily designed for direct interaction, MistralCowboy-7b-v0.1 can be adapted for tasks that benefit from a rugged, Western-themed narrative style.

Out-of-Scope Use

This model is not suited for modern, factual, or technical topics unrelated to the cowboy theme. Its expertise lies in historical and cultural representations of the cowboy era.

Bias, Risks, and Limitations

As it's trained on stylized, historical language, MistralCowboy-7b-v0.1 may reflect the biases and limitations of its era. This should be considered in educational and cultural settings.

Recommendations

Users are encouraged to provide context at the start of conversations for optimal performance. Custom stopping strings are advised to prevent overrun and maintain relevancy.

Custom Stopping Strings Usage

Recommended stopping strings include:

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

These aid in delineating responses and maintaining the cowboy dialogue structure.

Training Data

The model was trained on a mix of historical texts, cowboy literature, and scripted dialogues, ensuring a rich and authentic cowboy vocabulary and style.

Preprocessing

Datasets were formatted for consistent, structured input, focusing on emulating the cowboy communication style.

Training Hyperparameters

  • —Training Regime: FP32
  • —Warmup Steps: 1
  • —Per Device Train Batch Size: 1
  • —Gradient Accumulation Steps: 32
  • —Max Steps: 1000
  • —Learning Rate: 0.0002
  • —Logging Steps: 1
  • —Save Steps: 1
  • —Lora Alpha: 16
  • —Dimension Count: 8

Speeds, Sizes, Times

Training was completed in about 10 minutes using an RTX 6000 Ada GPU.

Testing Data

Evaluated against a range of cowboy-themed texts, demonstrating an excellent grasp of the style and content.

Factors

Focus on maintaining coherent, era-appropriate responses in cowboy vernacular.

Metrics

Emphasis was on model's ability to accurately and engagingly replicate cowboy speech.

Results

The model excels in consistently delivering authentic, engaging cowboy-style dialogues.

Performance Highlights

MistralCowboy-7b-v0.1 is our best performing model to date, expertly capturing the essence of cowboy talk in its responses.

Summary

MistralCowboy-7b-v0.1 is a significant leap forward in creating specialized language models that can authentically represent cultural and historical figures, in this case, the iconic American cowboy.

Model Architecture and Objective

Built upon the Mistral model architecture, further refined with LoRA modifications to encapsulate the cowboy communication style.

Compute Infrastructure

  • —Hardware Type: RTX 6000 Ada GPU
  • —Training Duration: Approximately 10 minutes

Acknowledgments

Special thanks to the Mistral team and OpenHermes 2.5 team. Our innovative use of LoRA techniques showcases the collaborative advancement in AI and language modeling, bringing the cowboy era to virtual life.