ermiaazarkhalili/Gemma4-E4B-SFT-Claude-Opus-Reasoning-Unsloth
Gemma4-E4B-SFT-Claude-Opus-Reasoning-Unsloth is a 4 billion parameter language model developed by ermiaazarkhalili, fine-tuned from Google's Gemma4-E4B-it. It is specifically optimized for reasoning distillation using chain-of-thought techniques, trained on a dataset of Claude's reasoning traces. This model leverages Unsloth for efficient training, offering 2x faster fine-tuning and 60% less VRAM usage, making it suitable for tasks requiring detailed step-by-step problem-solving within its 2,048 token context window.
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Overview
ermiaazarkhalili/Gemma4-E4B-SFT-Claude-Opus-Reasoning-Unsloth is a 4 billion parameter language model fine-tuned from Google's Gemma4-E4B-it. Developed by ermiaazarkhalili, this model specializes in reasoning distillation through chain-of-thought learning. It was trained using Unsloth, which significantly accelerates the fine-tuning process (2x faster) and reduces VRAM consumption (60% less).
Key Capabilities
- Enhanced Reasoning: Optimized for generating detailed, step-by-step reasoning processes, mimicking Claude's chain-of-thought. This is achieved by training on the claude-reasoning-distillation dataset, which includes over 10,000 samples with
<think>blocks. - Efficient Fine-tuning: Benefits from Unsloth's optimizations for faster training and lower memory footprint, making it accessible for researchers and developers with limited GPU resources.
- Gemma-4 Base: Built upon the Gemma-4-E4B-it architecture, providing a solid foundation for general language understanding and generation.
- Quantized Versions Available: Offers GGUF versions (Q4_K_M, Q5_K_M, Q8_0) for efficient deployment on CPUs and edge devices, compatible with tools like Ollama and llama.cpp.
Good For
- Problem Solving: Ideal for applications requiring models to explain their thought process or solve problems step-by-step.
- Educational Tools: Can be used to generate detailed explanations or tutoring responses that break down complex topics.
- Research in Reasoning: A strong candidate for exploring and developing chain-of-thought prompting and reasoning capabilities in smaller models.
- Resource-Constrained Environments: Suitable for users looking for a reasoning-capable model that can be efficiently fine-tuned and deployed due to its Unsloth optimization and GGUF availability.
Limitations: Primarily trained on English data, limited by the base model's knowledge cutoff, and has a fine-tuned context window of 2,048 tokens. It is not extensively safety-tuned.