Mistral-Zephyr-7B-slerp

This is a merge of pre-trained language models created using MergeKit, combining the foundational capabilities of Mistral-7B with Zephyr-7B's instruction-following improvements through an efficient SLERP fusion.

About Me

I'm David Soeiro-Vuong, a third-year Computer Science student working as an apprentice at TW3 Partners, a company specialized in Generative AI. Passionate about artificial intelligence and language models optimization, I focus on creating efficient model merges that balance performance and capabilities.

🔗 Connect with me on LinkedIn

Merge Details

Merge Method

This model uses SLERP (Spherical Linear Interpolation) with carefully tuned parameters to achieve optimal performance balance:

• Attention Layers: Variable interpolation values [0, 0.5, 0.3, 0.7, 1] leveraging Zephyr's strong instruction-following capabilities
• MLP Layers: Variable interpolation values [1, 0.5, 0.7, 0.3, 0] maintaining Mistral's reasoning capabilities
• Other Parameters: 0.5 interpolation value creating an equal blend for balanced performance
• Format: bfloat16 precision for efficient memory usage

Models Merged

• mistralai/Mistral-7B-v0.1 - The original Mistral model offering excellent base capabilities and innovative architecture
• HuggingFaceH4/zephyr-7b-beta - A fine-tuned version of Mistral optimized for following complex instructions

Configuration

slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 32]
      - model: HuggingFaceH4/zephyr-7b-beta
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
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

Model Capabilities

This merge combines:

• Mistral's strong foundational knowledge and reasoning
• Zephyr's improved instruction following and coherence
• Fully open architecture with no usage restrictions

The resulting model provides enhanced performance on tasks requiring both strong reasoning and good instruction following, such as:

• Detailed explanations of complex concepts
• Creative writing with coherent structure
• Problem-solving with step-by-step reasoning
• Balanced factual responses with nuanced perspectives

Limitations

• Inherits limitations from both base models
• May exhibit inconsistent behavior for certain complex reasoning tasks
• No additional alignment or fine-tuning beyond the base models' training
• Model was created through parameter merging without additional training data

License

This model is released under the Apache 2.0 license, consistent with the underlying models' licenses.