This article is designed to provide you with a step-by-step approach to using the Djuna L3.1-Ninomos model, a quantized AI model available through the Hugging Face platform. We’ll walk you through what you need to know and how to get started.
Understanding the Model
Imagine you have a library filled with books on every conceivable topic. Each book has been meticulously categorized, and only the most relevant texts are available to you based on your search queries. In the world of AI, the Djuna L3.1-Ninomos model is much like that library. It organizes the knowledge it has gathered and presents it to you in a concise manner, but made efficient and user-friendly through quantization.
Getting Started
Before we dive into usage, let’s summarize the key aspects of the model:
- Base Model: Djuna L3.1-Romes-Ninomos
- Library: Transformers
- Quantization Version: 2
How to Use the Model
Here’s a simple guide for using the model and its associated GGUF files:
- Download GGUF Files: Visit the links provided in the ‘Provided Quants’ section to download the necessary GGUF files. Be mindful of the file sizes and choose the one that suits your needs.
- Refer to Documentation: If you’re unsure about how to utilize GGUF files, check out [TheBlokes’ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for comprehensive details.
- Concatenate Multi-Part Files: If the model is spread across multiple GGUF files, you’ll need to concatenate them to create a complete model. Documentation will guide you through this process.
Available Quantized Files
The following quant files are available, sorted by size:
| Link | Type | Size (GB) | Notes |
|-------|--------|-----------|----------------------|
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.com/radermacher/L3.1-Romes-Ninomos-GGUF/resolvemain/L3.1-Romes-Ninomos.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Troubleshooting Guide
If you encounter any issues, here are some troubleshooting steps you can take:
- Missing Quant Files: If the weighted imatrix quants are not available, it may take up to a week for them to appear. If they do not show up, please feel free to request them by opening a Community Discussion.
- Documentation Confusion: If you have trouble navigating the documentation, don’t hesitate to revisit the links provided or contact community support.
- Performance Issues: Ensure that your system meets the requirements for running large GGUF files. Consider optimizing your setup for better performance.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
Utilizing the Djuna L3.1-Ninomos model can significantly enhance your AI projects. With quantized files making it more efficient, the process is streamlined. The extensive support available through community discussions and documentation ensures that help is just a click away.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
