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---
license: mit
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
- google/gemma-7b
library_name: transformers
tags:
- mergekit
- merged-model
- mistral
- gemma
- language-model
---
# 🚀 MistralGemma-Hybrid-7B: A Fusion of Power & Precision
## 📌 Overview
**MistralGemma-Hybrid-7B** is an **experimental hybrid language model** that blends the strengths of **Mistral-7B** and **Gemma-7B** using the **Spherical Linear Interpolation (slerp) merging technique**. Designed to optimize both efficiency and performance, this model offers robust text generation capabilities while leveraging the advantages of both parent models.
🔗 **Created by**: [Matteo Khan]
🎓 **Affiliation**: Apprentice at TW3 Partners (Generative AI Research)
📍 **License**: MIT
🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/matteo-khan-a10309263/)
🔗 [Model on Hugging Face](https://huggingface.co/YourProfile/MistralGemma-Hybrid-7B)
## 🧠 Model Details
- **Model Type**: Hybrid Language Model (Merged)
- **Parent Models**:
- [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- [Gemma-7B](https://huggingface.co/google/gemma-7b)
- **Merging Technique**: Slerp Merge (MergeKit)
## 🎯 Intended Use
This model is intended for **research and experimentation** in hybrid model optimization. Potential applications include:
- ✅ Text Generation
- ✅ Conversational AI
- ✅ Creative Writing Assistance
- ✅ Exploration of Model Merging Effects
## ⚠️ Limitations & Considerations
While **MistralGemma-Hybrid-7B** offers enhanced capabilities, it also inherits limitations from its parent models:
- ❌ May generate **inaccurate or misleading** information
- ⚠️ Potential for **biased, offensive, or harmful** content
- 🔄 Merging may introduce **unpredictable behaviors**
- 📉 Performance may **vary across different tasks**
## 🔬 Merging Process & Configuration
This is **not a newly trained model**, but rather a merge of existing models using the following configuration:
```yaml
merge_method: slerp # Using slerp instead of linear
dtype: float16
models:
- model: "mistralai/Mistral-7B-v0.1"
parameters:
weight: 0.5
- model: "google/gemma-7b"
parameters:
weight: 0.5
parameters:
normalize: true
int8_mask: false
rescale: true # Helps with different model scales
layers:
- pattern: ".*"
layer_range: [0, -1]
```
📊 **No formal evaluation** has been conducted yet. Users are encouraged to **benchmark and share feedback**!
## 🌍 Environmental Impact
By utilizing **model merging** rather than training from scratch, **MistralGemma-Hybrid-7B** significantly reduces computational and environmental costs.
## 🚀 How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "YourProfile/MistralGemma-Hybrid-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Write a short story about the future of AI."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
**📝 Citation**
```bibtex
@misc{mistralgemma2025,
title={MistralGemma: A Hybrid Open-Source Language Model},
author={Your Name},
year={2025},
eprint={arXiv:XXXX.XXXXX},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
📩 **Feedback & Contact**: Reach out via [Hugging Face](https://huggingface.co/YourProfile).
🎉 **Happy Experimenting!** 🚀 |