---
license: apache-2.0
tags:
- merge
- mergekit
- 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:

- [**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.

## 🧩 Merge 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
        layer_range: [0, 32]
      - model: Nitral-Archive/Prima-Pastacles-7b
        layer_range: [0, 32]
merge_method: slerp
base_model: hydra-project/ChatHercules-2.5-Mistral-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      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`

---