prithivMLmods/Sombrero-Opus-14B-Sm4

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Mar 6, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

Sombrero-Opus-14B-Sm4, developed by prithivMLmods, is a 14 billion parameter language model based on the Qwen 2.5 architecture. It is specifically optimized for coding efficiency, computational reasoning, and mathematical problem-solving, supporting a long context of up to 128K tokens for input and 8K tokens for output. This model excels in generating structured code, providing technical explanations, and debugging assistance, while minimizing unwanted textual token generation for focused coding tasks.

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Sombrero-Opus-14B-Sm4: Enhanced for Coding and Reasoning

Sombrero-Opus-14B-Sm4, developed by prithivMLmods, is a 14 billion parameter model built on the Qwen 2.5 architecture. It is meticulously fine-tuned to boost coding efficiency and computational reasoning, distinguishing itself through optimized memory usage and a focus on generating precise, structured outputs.

Key Capabilities

  • Optimized for Coding: Specializes in generating high-quality, structured code with minimal redundant tokens.
  • Enhanced Memory Utilization: Features streamlined memory optimization for reduced computational overhead.
  • Superior Reasoning: Excels in complex mathematical and algorithmic problem-solving with logical explanations.
  • Long-Context Support: Handles up to 128K input tokens and generates up to 8K output tokens, ideal for detailed coding responses.
  • Focused Output: Minimizes excessive textual responses to ensure more relevant output for coding tasks.

Good for

  • Code Generation & Optimization: Assisting developers in writing, refactoring, and optimizing code across various languages.
  • Algorithm & Mathematical Problem Solving: Providing precise explanations and solutions for computational and mathematical challenges.
  • Technical Explanations & Documentation: Generating clear and structured explanations for coding concepts, libraries, and APIs.
  • Debugging Assistance: Analyzing code snippets, detecting errors, and suggesting corrections.
  • Structured Data Processing: Analyzing and generating structured outputs like JSON, XML, and tables for data science applications.

Limitations

  • Requires high-memory GPUs or TPUs due to its size and long-context support.
  • May exhibit potential biases from training data and inconsistent outputs in creative, non-technical tasks.
  • Effectiveness is sensitive to prompt structure.

Finetunes
1 models