SrCh1nask1/X-Machina-7b-slerp-v0.0

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kTool Calling:SupportedPublished:Apr 8, 2024License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

SrCh1nask1/X-Machina-7b-slerp-v0.0 is a 7 billion parameter language model created by SrCh1nask1, developed by merging occiglot/occiglot-7b-es-en-instruct and chihoonlee10/T3Q-DPO-Mistral-7B using a slerp method. This model combines the capabilities of its base models, offering a blend of instruction-following and potentially improved performance from the DPO-tuned Mistral variant. It is suitable for general text generation tasks within its 4096 token context window.

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Model Overview

X-Machina-7b-slerp-v0.0 is a 7 billion parameter language model developed by SrCh1nask1. It was created through a spherical linear interpolation (slerp) merge of two distinct base models: occiglot/occiglot-7b-es-en-instruct and chihoonlee10/T3Q-DPO-Mistral-7B. This merging technique aims to combine the strengths of both foundational models.

Key Characteristics

  • Architecture: Based on the Mistral architecture, inheriting its efficiency and performance characteristics.
  • Merging Method: Utilizes slerp (spherical linear interpolation) to blend the weights of the two source models, specifically targeting different layers (self-attention and MLP) with varying interpolation values.
  • Base Models: Integrates an instruction-tuned model (occiglot/occiglot-7b-es-en-instruct) and a DPO-tuned Mistral variant (chihoonlee10/T3Q-DPO-Mistral-7B), suggesting a focus on instruction following and refined output quality.
  • Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a context window of 4096 tokens.

Potential Use Cases

This model is suitable for a range of natural language processing tasks, particularly those benefiting from instruction-following capabilities and the general improvements offered by DPO tuning. Developers can leverage it for:

  • General text generation and completion.
  • Instruction-based tasks and conversational AI.
  • Applications requiring a blend of multilingual understanding (from the occiglot base) and refined response generation.

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