DireDreadlord/GemCod-R-Sapphire-270M

TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.3BQuant:BF16Context Size:32kPublished:Jun 11, 2026License:gemmaArchitecture:Transformer Featherless Exclusive Cold

DireDreadlord/GemCod-R-Sapphire-270M is a 270 million parameter Gemma3-based model developed by DireDreadlord, specifically fine-tuned for code generation. This model integrates Chain of Thought (COT) reasoning capabilities to provide accurate, hallucination-free code snippets and long-form code, along with detailed explanations. It excels at generating code in all major programming languages and offers rudimentary Q/A on code-related subjects, designed to run efficiently on laptop-grade GPUs.

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GemCod-R-Sapphire-270M Overview

DireDreadlord/GemCod-R-Sapphire-270M is a compact, 270 million parameter model built on the Gemma3 architecture, specifically designed for code generation and reasoning. It is an evolution within the GemCod-R family, enhancing previous models like Jade and Topaz by integrating Chain of Thought (COT) reasoning. This integration significantly reduces hallucinations and enables the model to provide highly detailed explanations and instructions alongside its code generations.

Key Capabilities

  • Accurate Code Generation: Produces reliable code snippets and long-form code in various programming languages.
  • Chain of Thought (COT) Reasoning: Incorporates step-by-step reasoning to ensure high-quality, hallucination-free outputs and specialized explanations.
  • Instruction Generation: Capable of generating instructions for code-related tasks.
  • Code-Related Q/A: Offers basic question-answering and subject matter expert capabilities on coding topics.
  • Lightweight Design: Its small size allows for comfortable execution on laptop-grade GPUs.

Good For

  • Developers needing quick and accurate code snippets or longer code blocks.
  • Users requiring detailed explanations and reasoning alongside generated code.
  • Environments with limited computational resources, such as laptop GPUs.

Training Details

The model was fine-tuned using Supervised Fine-Tuning (SFT) on the code-reasoning-4k dataset, comprising approximately 40,000 templated rows. Training was conducted for 3,000 steps on an RTX 3050 (4GB VRAM).