TinyStable-Hybrid-1.6B

🚀 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
🔍 Model on Hugging Face

🧠 Model Details

• Model Type: Hybrid Language Model (Merged)
• Parent Models:
  • TinyLlama-1.1B-Chat-v1.0
  • 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:

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

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

@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.

🎉 Happy Experimenting! 🚀