---
license: mit
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
library_name: transformers
tags:
- mergekit
- merged-model
- tinyllama
- stablelm
- language-model
---

# 🚀 TinyStable-Hybrid-1.6B: Merging Efficiency & Power

## 📌 Overview
**TinyStable-Hybrid-1.6B** is an **experimental hybrid language model** that merges the capabilities of TinyLlama and StableLM. Built using **MergeKit**, this model is designed to balance performance and efficiency while offering strong text generation capabilities.

🔗 **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/MatteoKhan/TinyStable-Hybrid-1.6B)  

## 🧠 Model Details
- **Model Type**: Hybrid Language Model (Merged)
- **Parent Models**:
  - [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
  - [TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
- **Merging Technique**: Linear Merge (MergeKit)

## 🎯 Intended Use
This model is primarily intended for **research and experimentation** in hybrid model optimization. Potential use cases include:
- ✅ Text Generation
- ✅ Conversational AI
- ✅ Creative Writing Assistance
- ✅ Exploration of Model Merging Effects

## ⚠️ Limitations & Considerations
While **TinyStable-Hybrid-1.6B** offers enhanced capabilities, it also inherits certain 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: linear
dtype: float16
models:
  - model: "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
    parameters:
      t: 1.0
      weight: 0.5
  - model: "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
    parameters:
      t: 1.0
      weight: 0.5
parameters:
  normalize: true
  int8_mask: false
layers:
  - pattern: "model.*"
```

📊 **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, **TinyStable-Hybrid-1.6B** significantly reduces computational and environmental costs.

## 🚀 How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "MatteoKhan/TinyStable-Hybrid-1.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
prompt = "Write a short poem about artificial intelligence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
**📝 TinyLlama**  
```bibtex
@misc{zhang2024tinyllama,
      title={TinyLlama: An Open-Source Small Language Model},
      author={Jiayu Zhang and others},
      year={2024},
      eprint={2401.02385},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

📩 **Feedback & Contact**: Reach out via [Hugging Face](https://huggingface.co/MatteoKhan).

🎉 **Happy Experimenting!** 🚀