DavidAU/Llama-3.1-128k-Dark-Planet-Uncensored-8B
DavidAU/Llama-3.1-128k-Dark-Planet-Uncensored-8B is an 8 billion parameter Llama-3.1 based model designed for full precision source code generation, enabling conversion to various quantized formats like GGUF, GPTQ, and EXL2. This model emphasizes optimal operation through specific parameter and sampler settings, detailed in an external guide, to enhance performance across diverse use cases. It is particularly noted for its flexibility in generating different quantization formats and its reliance on advanced configuration for peak performance.
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Llama-3.1-128k-Dark-Planet-Uncensored-8B Overview
This model, developed by DavidAU, is an 8 billion parameter variant based on the Llama-3.1 architecture. It is provided as full-precision source code in "safe tensors" format, primarily intended for generating various quantized versions such as GGUF, GPTQ, EXL2, AWQ, and HQQ. The model is classified as a "Class 3/4" model, indicating that its optimal performance heavily relies on specific parameter, sampler, and advanced sampler settings.
Key Characteristics & Usage
- Source Code Provision: Distributed as full-precision source code, allowing for flexible conversion to multiple quantization formats.
- Performance Optimization: Emphasizes the critical role of specific parameter and sampler settings for achieving optimal operation across different AI/LLM applications. Users are directed to an external guide for detailed configuration.
- Versatility: The recommended settings are not only for this model but are also suggested for enhancing the operation of any model, from any repository, and any quant type.
- Community Contributions: Acknowledges the contributions of various tools and communities, including Huggingface, LlamaCPP, MergeKit, LM Studio, Text Generation Webui, KolboldCPP, and SillyTavern, which facilitate model development, quantization, testing, and deployment.
Important Considerations
Users are strongly advised to consult the provided external documentation for critical parameter and sampler settings to maximize performance, especially for use cases beyond the model's default design or when encountering sub-par operation with default application settings. Further details, including context limits, special usage notes, and GGUF quants, are available on the dedicated GGUF repository.