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README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- lazymergekit
+- mistralai/Mistral-Nemo-Instruct-2407
+- mistralai/Mistral-Nemo-Base-2407
+---
+# Quantized GGUF model Mistral-Nemo-Instruct-2407-Mistral-Nemo-Base-2407-linear-merge
+This model has been quantized using llama-quantize from [llama.cpp](https://github.com/ggerganov/llama.cpp)
+    
+
+# Mistral-Nemo-Instruct-2407-Mistral-Nemo-Base-2407-linear-merge
+
+Mistral-Nemo-Instruct-2407-Mistral-Nemo-Base-2407-linear-merge is a sophisticated language model created by merging two distinct models: [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) and [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407). This merging process was executed using [mergekit](https://github.com/cg123/mergekit), a specialized tool designed for precise model blending, ensuring optimal performance and synergy between the merged architectures.
+
+## 🧩 Merge Configuration
+
+The models were merged using a linear interpolation method, which allows for a balanced integration of the two models. The configuration details are as follows:
+
+```yaml
+models:
+  - model: mistralai/Mistral-Nemo-Instruct-2407
+    parameters:
+      weight: 0.6
+  - model: mistralai/Mistral-Nemo-Base-2407
+    parameters:
+      weight: 0.4
+merge_method: linear
+parameters:
+  normalize: true
+dtype: float16
+```
+
+## Model Features
+
+This merged model combines the instructive capabilities of Mistral-Nemo-Instruct-2407 with the foundational strengths of Mistral-Nemo-Base-2407. The result is a versatile model that excels in various text generation tasks, offering enhanced context understanding and nuanced text generation. By leveraging the strengths of both parent models, this linear merge aims to provide improved performance across diverse NLP applications.
+
+## Limitations
+
+While the merged model benefits from the strengths of both parent models, it may also inherit certain limitations or biases present in them. Users should be aware that the performance can vary depending on the specific task and context, and it is advisable to evaluate the model's outputs critically.