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--- |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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license: llama3.1 |
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pipeline_tag: text-generation |
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base_model: |
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- OpenBuddy/openbuddy-llama3.1-8b-v22.2-131k |
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- THUDM/LongWriter-llama3.1-8b |
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- akjindal53244/Llama-3.1-Storm-8B |
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- aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored |
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- ValiantLabs/Llama3.1-8B-Enigma |
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- agentlans/Llama3.1-vodka |
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--- |
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# Llama3.1-Dark-Enigma |
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## Model Description |
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Llama3.1-Dark-Enigma is a hybrid AI text model designed for diverse tasks such as research analysis, writing, editing, role-playing, and coding. |
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## Intended Use |
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This model can be used in various applications where natural language processing (NLP) capabilities are required. It's particularly useful for: |
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- Research: Analyzing textual data, planning experiments, or brainstorming ideas. |
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- Writing and Editing: Generating text, proofreading content, or suggesting improvements. |
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- Role-playing: Simulating conversations or scenarios to enhance creativity. |
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- Coding: Assisting with programming tasks due to its ability to understand code-like language. |
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## Data Overview |
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The model is built by merging several Llama 3.1 8B text models selected for their diverse layer weights. This fusion aims to leverage the strengths of each component, resulting in a more robust and versatile AI tool. |
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- [OpenBuddy/openbuddy-llama3.1-8b-v22.2-131k](https://huggingface.co/OpenBuddy/openbuddy-llama3.1-8b-v22.2-131k) |
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- [THUDM/LongWriter-llama3.1-8b](https://huggingface.co/THUDM/LongWriter-llama3.1-8b) |
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- [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) |
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- [aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored](https://huggingface.co/aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored) |
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- [ValiantLabs/Llama3.1-8B-Enigma](https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma) |
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Those models were merged onto [agentlans/Llama3.1-vodka](https://huggingface.co/agentlans/Llama3.1-vodka) using [mergekit](https://github.com/arcee-ai/mergekit)'s `model_stock` method where each model is equally weighted. |
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## Performance Evaluation |
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While specific performance metrics are not provided, users can expect high-quality output when using effective prompting techniques and grounded input texts. The model's uncensored nature ensures it doesn't shy away from complex or sensitive topics. |
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In fact, most of this model card was generated by the model itself. |
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## Limitations |
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Users should note the following: |
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- Do not rely solely on the model's output. Always validate its results. |
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- As a 8B parameter model, it does poorly on closed book factual questions and answers. |
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- For optimal performance, use good prompting strategies to guide the model effectively. |
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- Be cautious when processing text that may contain biases or inaccuracies. |
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- The model can't connect to the Internet and it doesn't know how to use specific APIs, libraries, or frameworks. |
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## Bias and Fairness Analysis |
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The model has been designed with diversity in mind by merging multiple component models. However, as with any AI system, there is a risk of perpetuating existing biases if not used responsibly. Users should be aware of these potential issues and strive to mitigate them through careful input selection and post-processing. |
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## Recommendations for Responsible Use |
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To ensure the responsible use of Llama3.1-Dark-Enigma: |
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- Always validate the model's output. |
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- Use grounded, relevant input texts when processing information. |
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- Be mindful of the model's limitations and potential biases. |
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- Continuously monitor and update your knowledge to stay informed about best practices in AI ethics. |
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- Finally, respect Meta's Llama 3.1 usage terms. |
