redpooh87/myqwen2_5-14b-instrut_coder-0707-124928

TEXT GENERATIONConcurrent Unit Cost:1Model Size:14.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 7, 2026Architecture:Transformer Featherless Exclusive Cold

redpooh87/myqwen2_5-14b-instrut_coder-0707-124928 is a 14.8 billion parameter language model, merged from Qwen2.5-Coder-14B-Instruct and Qwen2.5-14B-Instruct using the SLERP method. This model combines the general instruction-following capabilities of Qwen2.5-14B-Instruct with the specialized coding proficiency of Qwen2.5-Coder-14B-Instruct. It is designed for tasks requiring both broad language understanding and strong code generation or comprehension, with a context length of 32768 tokens.

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Model Overview

redpooh87/myqwen2_5-14b-instrut_coder-0707-124928 is a 14.8 billion parameter language model created by merging two specialized Qwen2.5 models: Qwen/Qwen2.5-Coder-14B-Instruct and Qwen/Qwen2.5-14B-Instruct. This merge was performed using the SLERP (Spherical Linear Interpolation) method, which blends the weights of the base models to combine their strengths.

Key Capabilities

  • Hybrid Performance: Leverages the general instruction-following abilities of Qwen2.5-14B-Instruct and the enhanced code generation and understanding from Qwen2.5-Coder-14B-Instruct.
  • Code-Oriented Tasks: Expected to perform well on programming-related queries, code completion, debugging, and explanation, while retaining strong natural language capabilities.
  • Instruction Following: Designed to accurately follow user instructions for a wide range of tasks.
  • Context Length: Supports a substantial context window of 32768 tokens, suitable for handling longer code snippets or complex conversational histories.

When to Use This Model

This model is particularly well-suited for applications that require a balance between general-purpose language understanding and specialized coding skills. Consider using it for:

  • Software Development Assistants: Building tools for code generation, review, or explanation.
  • Technical Q&A: Answering questions that involve both natural language and programming concepts.
  • Multi-modal Code Tasks: Scenarios where understanding natural language instructions is crucial for generating or manipulating code.
  • Educational Tools: Assisting learners with programming concepts and problem-solving.

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