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README.md

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 ## Model Features
 
-This merged model combines the advanced capabilities of the HQQ quantized version of Llama-3.1-8B-Instruct with the fine-tuned prowess of the SauerkrautLM variant. The result is a versatile model that excels in both generative tasks and nuanced understanding of multilingual contexts, particularly in German and English. The model is designed to handle a variety of text generation tasks, making it suitable for applications ranging from conversational agents to content creation.
+This merged model combines the advanced capabilities of the HQQ quantized version of Llama-3.1-8B-Instruct with the fine-tuned prowess of Llama-3.1-SauerkrautLM-8b-Instruct. The result is a versatile model that excels in both generative tasks and nuanced understanding of multilingual contexts, particularly in German and English. The integration of Spectrum Fine-Tuning from the SauerkrautLM model enhances its efficiency and performance, making it suitable for a wide range of applications.
 
 ## Evaluation Results
 
-The individual models have demonstrated impressive performance across various benchmarks:
+The evaluation results from the parent models indicate strong performance across various benchmarks. For instance, the HQQ 4-bit version of Llama-3.1-8B-Instruct achieved notable scores on tasks like ARC, HellaSwag, and MMLU, while the SauerkrautLM model demonstrated significant improvements in multilingual capabilities. The combined strengths of these models in the merged version are expected to yield even better results in similar tasks.
 
-- **mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib**:
-  - ARC (25-shot): 60.49
-  - HellaSwag (10-shot): 80.16
-  - MMLU (5-shot): 68.98
-  - Average performance: 69.51
-
-- **VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct**:
-  - Fine-tuned on German-English data, showcasing significant improvements in multilingual capabilities.
-
-The merged model inherits the strengths of both parent models, providing enhanced performance in multilingual contexts while maintaining the efficiency of the HQQ quantization.
+| Benchmark         | Llama-3.1-8B-Instruct (HQQ 4-bit) | Llama-3.1-SauerkrautLM |
+|-------------------|-----------------------------------|-------------------------|
+| ARC (25-shot)     | 60.32                             | Not specified           |
+| HellaSwag (10-shot)| 79.21                            | Not specified           |
+| MMLU (5-shot)     | 67.07                             | Not specified           |
+| Average           | 68.00                             | Not specified           |
 
 ## Limitations
 
-While the merged model benefits from the strengths of both parent models, it may also carry over some limitations. For instance, the potential for uncensored content remains a concern, as noted in the SauerkrautLM documentation. Additionally, the model's performance may vary depending on the specific task and the quality of the input data. Users should be aware of these factors when deploying the model in real-world applications.
+While the merged model benefits from the strengths of both parent models, it may also inherit some limitations. For instance, the HQQ quantization process could introduce certain biases or inaccuracies in specific contexts, and the fine-tuning on a limited dataset may not cover all nuances of the languages involved. Users should be aware of these potential issues and exercise caution when deploying the model in sensitive applications. Additionally, despite efforts to ensure appropriate behavior, the possibility of encountering uncensored content remains.
+
+In summary, Llama-3.1-8b-instruct_4bitgs64_hqq_calib-Llama-3.1-SauerkrautLM-8b-Instruct-dare-merge represents a significant advancement in language modeling, combining the best features of its predecessors while also carrying forward some of their limitations.