mergekit-community/Qwen2-1.5B-RHSD

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 12, 2024Architecture:Transformer0.0K Featherless Exclusive Warm

Qwen2-1.5B-RHSD is a 1.5 billion parameter language model created by mergekit-community, utilizing the Qwen2 architecture. This model is a merge of four specialized Qwen2-1.5B variants, including those focused on coding, instruction following, and conversational AI. It was developed using the Model Stock merge method, building upon trollek/Qwen2-1.5B-Instruct-Abliterated as its base. The model is designed to combine diverse capabilities from its constituent models, offering a versatile solution for various natural language processing tasks.

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Model Overview

Qwen2-1.5B-RHSD is a 1.5 billion parameter language model developed by mergekit-community using the mergekit tool. It leverages the Qwen2 architecture and was created using the advanced Model Stock merge method.

Key Capabilities

This model is a composite of several specialized Qwen2-1.5B models, aiming to integrate their strengths. The merged components include:

  • Replete-AI/Replete-Coder-Qwen2-1.5b: Likely contributes to code generation and understanding capabilities.
  • cognitivecomputations/dolphin-2.9.3-qwen2-1.5b: Suggests enhanced instruction following and conversational abilities.
  • macadeliccc/Samantha-Qwen2-1.5B: Implies a focus on specific conversational or persona-based interactions.
  • M4-ai/Hercules-5.0-Qwen2-1.5B: Indicates general strong performance or specific domain expertise.

By combining these models, Qwen2-1.5B-RHSD is intended to offer a broad range of functionalities, from coding assistance to nuanced conversational interactions, all within a compact 1.5 billion parameter footprint and supporting a 32768 token context length.

Good For

  • Versatile applications: Due to its merged nature, it's suitable for tasks requiring a blend of coding, instruction following, and conversational skills.
  • Resource-constrained environments: Its 1.5B parameter size makes it efficient for deployment where larger models are impractical.
  • Experimentation with merged models: Provides a practical example of the Model Stock merging technique for developers interested in model fusion.

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2 models