jinnam12/Qwen2_5-14B-9-1-0708-095147

TEXT GENERATIONConcurrent Unit Cost:1Model Size:14.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 8, 2026Architecture:Transformer Featherless Exclusive Cold

The jinnam12/Qwen2_5-14B-9-1-0708-095147 model is a 14.8 billion parameter language model, merged from Qwen/Qwen2.5-14B-Instruct and Qwen/Qwen2.5-Coder-14B-Instruct using the SLERP method. This merge aims to combine general instruction-following capabilities with enhanced coding proficiency. With a 32768 token context length, it is designed for tasks requiring both broad understanding and specialized code generation or analysis.

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Model Overview

This model, jinnam12/Qwen2_5-14B-9-1-0708-095147, is a 14.8 billion parameter language model created through a strategic merge of two base models: Qwen/Qwen2.5-14B-Instruct and Qwen/Qwen2.5-Coder-14B-Instruct. The merge was performed using the SLERP (Spherical Linear Interpolation) method, a technique often employed to combine the strengths of different pre-trained models while maintaining coherence.

Key Capabilities

  • Hybrid Performance: By merging a general instruction-tuned model with a coder-specific variant, this model is engineered to offer a balanced performance across diverse tasks.
  • Enhanced Coding: The inclusion of Qwen/Qwen2.5-Coder-14B-Instruct suggests improved capabilities in code generation, understanding, and related programming tasks.
  • General Instruction Following: Retains the instruction-following prowess from the base Qwen/Qwen2.5-14B-Instruct model, making it suitable for a wide array of natural language processing applications.
  • Context Length: Supports a substantial context window of 32768 tokens, beneficial for handling longer inputs and generating more extensive outputs.

When to Use This Model

This model is particularly well-suited for use cases that require a combination of:

  • General-purpose AI assistance: For tasks like summarization, question answering, and content generation.
  • Code-related applications: Such as generating code snippets, debugging assistance, explaining code, or translating between programming languages.
  • Complex tasks: Where both broad linguistic understanding and specialized technical knowledge, especially in coding, are advantageous.

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