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README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- lazymergekit
+- NousResearch/Hermes-2-Pro-Mistral-7B
+- mistralai/Mistral-7B-Instruct-v0.3
+---
+# Quantized GGUF model Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge
+This model has been quantized using llama-quantize from [llama.cpp](https://github.com/ggerganov/llama.cpp)
+    
+
+# Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge
+
+Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
+* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
+* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
+
+## 🧩 Merge Configuration
+
+```yaml
+merge_method: linear
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+models:
+  - model: NousResearch/Hermes-2-Pro-Mistral-7B
+    parameters:
+      weight: 0.3
+  - model: mistralai/Mistral-7B-Instruct-v0.3
+    parameters:
+      weight: 0.7
+parameters:
+  normalize: true
+dtype: float16
+```
+
+## Model Description
+
+The Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge combines the advanced conversational capabilities of the Hermes 2 Pro model with the instruction-following prowess of the Mistral-7B-Instruct model. This strategic fusion aims to enhance the model's ability to understand and generate contextually relevant responses while maintaining a high level of performance across various natural language processing tasks.
+
+Hermes 2 Pro is an upgraded version of the original Nous Hermes 2, featuring a refined dataset and improved function calling capabilities. It excels in generating structured outputs, making it particularly useful for applications requiring precise data formatting, such as JSON responses. The Mistral-7B-Instruct model, on the other hand, is designed to follow instructions effectively, making it a strong candidate for tasks that require adherence to user prompts.
+
+## Use Cases
+
+This merged model is well-suited for a variety of applications, including but not limited to:
+- Conversational agents and chatbots
+- Function calling and structured data generation
+- Instruction-based tasks and question answering
+- Creative writing and storytelling
+
+## Model Features
+
+- **Enhanced Conversational Abilities**: The model leverages the conversational strengths of Hermes 2 Pro, allowing for engaging and context-aware dialogues.
+- **Instruction Following**: With the integration of Mistral-7B-Instruct, the model can effectively follow user instructions, making it ideal for task-oriented applications.
+- **Function Calling and JSON Outputs**: The model supports advanced function calling and can generate structured JSON outputs, facilitating integration with various applications and APIs.
+
+## Evaluation Results
+
+The performance of the parent models provides a solid foundation for the merged model. Here are some evaluation metrics from the original models:
+
+### Hermes 2 Pro
+- **Function Calling Accuracy**: 91%
+- **JSON Mode Accuracy**: 84%
+
+### Mistral-7B-Instruct
+While specific evaluation metrics for Mistral-7B-Instruct were not available, it is known for its strong instruction-following capabilities, which contribute to the overall performance of the merged model.
+
+## Limitations
+
+Despite the strengths of the merged model, it may inherit some limitations from its parent models. Potential issues include:
+- **Biases**: The model may reflect biases present in the training data of both parent models, which could affect the fairness and neutrality of its outputs.
+- **Contextual Understanding**: While the model excels in many areas, there may still be challenges in understanding highly nuanced or ambiguous prompts.
+
+In summary, the Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge represents a powerful tool for a wide range of NLP tasks, combining the best features of its parent models while also carrying forward some of their limitations.