Update README.md

README.md

@@ -6,17 +6,30 @@ tags:
 - lazymergekit
 - hydra-project/ChatHercules-2.5-Mistral-7B
 - Nitral-Archive/Prima-Pastacles-7b
+language:
+- en
+base_model:
+- hydra-project/ChatHercules-2.5-Mistral-7B
+- Nitral-Archive/Prima-Pastacles-7b
+library_name: transformers
 ---
+# Mistral-2.5-Prima-Hercules-Fusion-7B
+
+**Mistral-2.5-Prima-Hercules-Fusion-7B** is a sophisticated language model crafted by merging **hydra-project/ChatHercules-2.5-Mistral-7B** with **Nitral-Archive/Prima-Pastacles-7b** using the **spherical linear interpolation (SLERP)** method. This fusion leverages the conversational depth of Hercules and the contextual adaptability of Prima, resulting in a model that excels in dynamic assistant applications and multi-turn conversations.
+
+## 🚀 Merged Models
+
+This model merge incorporates the following:
 
-# ZeroXClem/Mistral-2.5-Prima-Hercules-Fusion-7B
+- [**hydra-project/ChatHercules-2.5-Mistral-7B**](https://huggingface.co/hydra-project/ChatHercules-2.5-Mistral-7B): Serves as the primary model, renowned for its exceptional conversational abilities and robust language comprehension.
+- [**Nitral-Archive/Prima-Pastacles-7b**](https://huggingface.co/Nitral-Archive/Prima-Pastacles-7b): Enhances contextual adaptability and task-switching capabilities, providing intuitive context management for diverse applications.
 
-ZeroXClem/Mistral-2.5-Prima-Hercules-Fusion-7B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
-* [hydra-project/ChatHercules-2.5-Mistral-7B](https://huggingface.co/hydra-project/ChatHercules-2.5-Mistral-7B)
-* [Nitral-Archive/Prima-Pastacles-7b](https://huggingface.co/Nitral-Archive/Prima-Pastacles-7b)
+## 🧩 Merge Configuration
 
-## 🧩 Configuration
+The configuration below outlines how the models are merged using **spherical linear interpolation (SLERP)**. This method ensures a seamless blend of architectural layers from both source models, optimizing their unique strengths for enhanced performance.
 
 ```yaml
+# Mistral-2.5-Prima-Hercules-Fusion-7B Merge Configuration
 slices:
   - sources:
       - model: hydra-project/ChatHercules-2.5-Mistral-7B
@@ -33,5 +46,107 @@ parameters:
       value: [1, 0.5, 0.7, 0.3, 0]
     - value: 0.5
 dtype: bfloat16
+```
+
+### Key Parameters
+
+- **Self-Attention Filtering** (`self_attn`): Modulates the blending across self-attention layers, allowing the model to balance attention mechanisms from both source models effectively.
+- **MLP Filtering** (`mlp`): Fine-tunes the integration within Multi-Layer Perceptrons, ensuring optimal neural network layer performance.
+- **Global Weight (`t.value`)**: Applies a universal interpolation factor to layers not explicitly filtered, maintaining an even blend between models.
+- **Data Type (`dtype`)**: Utilizes `bfloat16` to maintain computational efficiency while preserving high precision.
+
+## 🏆 Performance Highlights
+
+- **Enhanced Multi-Turn Conversation Handling**: Improved context retention facilitates more coherent and contextually aware multi-turn interactions.
+- **Dynamic Assistant Applications**: Excels in role-play and scenario-based interactions, providing nuanced and adaptable responses.
+- **Balanced Integration**: Combines the conversational depth of Hercules with the contextual adaptability of Prima for versatile performance across various tasks.
+
+## 🎯 Use Case & Applications
+
+**Mistral-2.5-Prima-Hercules-Fusion-7B** is designed to excel in environments that demand both conversational prowess and specialized task execution. Ideal applications include:
+
+- **Advanced Conversational Agents**: Powering chatbots and virtual assistants with nuanced understanding and responsive capabilities.
+- **Educational Tools**: Assisting in tutoring systems, providing explanations, and facilitating interactive learning experiences.
+- **Content Generation**: Creating high-quality, contextually relevant content for blogs, articles, and marketing materials.
+- **Technical Support**: Offering precise and efficient support in specialized domains such as IT, healthcare, and finance.
+- **Role-Playing Scenarios**: Enhancing interactive storytelling and simulation-based training with dynamic and contextually aware responses.
+
+## 📝 Usage
+
+To utilize **Mistral-2.5-Prima-Hercules-Fusion-7B**, follow the steps below:
+
+### Installation
+
+First, install the necessary libraries:
+
+```bash
+pip install -qU transformers accelerate
+```
+
+### Inference
+
+Below is an example of how to load and use the model for text generation:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Define the model name
+model_name = "ZeroXClem/Mistral-2.5-Prima-Hercules-Fusion-7B"
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Initialize the pipeline
+text_generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Define the input prompt
+prompt = "Explain the significance of artificial intelligence in modern healthcare."
+
+# Generate the output
+outputs = text_generator(
+    prompt,
+    max_new_tokens=150,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
+)
+
+# Print the generated text
+print(outputs[0]["generated_text"])
+```
+
+### Notes
+
+- **Fine-Tuning**: This merged model requires fine-tuning for optimal performance in specific applications.
+- **Resource Requirements**: Ensure that your environment has sufficient computational resources, especially if deploying on GPU-enabled hardware for faster inference.
+
+
+## 📜 License
+
+This model is open-sourced under the **Apache-2.0 License**.
+
+## 💡 Tags
+
+- `merge`
+- `mergekit`
+- `slerp`
+- `Mistral`
+- `hydra-project/ChatHercules-2.5-Mistral-7B`
+- `Nitral-Archive/Prima-Pastacles-7b`
 
-```
+---