🚀 CerebrasOPT-Hybrid-6.7B: A Balanced Fusion of Strength & Efficiency

📌 Overview

CerebrasOPT-Hybrid-6.7B is an experimental hybrid language model that merges the capabilities of Cerebras-GPT-6.7B and OPT-6.7B using the Linear Merge technique. This approach aims to enhance performance while maintaining efficiency, leveraging the best of both parent models.

🔗 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:
- Cerebras-GPT-6.7B
- OPT-6.7B
Merging Technique: Linear Merge (MergeKit)

🎯 Intended Use

This model is primarily intended for research and experimentation in hybrid model optimization. Possible applications include:

✅ Text Generation
✅ Conversational AI
✅ Creative Writing Assistance
✅ Exploration of Model Merging Effects

⚠️ Limitations & Considerations

While CerebrasOPT-Hybrid-6.7B provides 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: "cerebras/Cerebras-GPT-6.7B"
    parameters:
      t: 1.0
      weight: 0.5
  - model: "facebook/opt-6.7b"
    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 instead of training from scratch, CerebrasOPT-Hybrid-6.7B significantly reduces computational and environmental costs.

🚀 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "YourProfile/CerebrasOPT-Hybrid-6.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
prompt = "Describe the future of AI in a short paragraph."
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

@misc{cerebrasopt2025,
      title={CerebrasOPT: 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.

🎉 Happy Experimenting! 🚀

MatteoKhan
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Cerebras-OPT-Fusion