chanifrusydi/gemma-3-270m-lora-finetuned
chanifrusydi/gemma-3-270m-lora-finetuned is a 0.3 billion parameter Gemma 3 model, fine-tuned with LoRA adapters on approximately 100,000 instruction-following samples from the mlabonne/FineTome-100k dataset. This model is optimized for instruction following, conversational AI, question answering, and text generation tasks. It supports a context length of 32768 tokens and is available in Safetensors and GGUF formats for various deployment scenarios.
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Overview
This model, chanifrusydi/gemma-3-270m-lora-finetuned, is a compact yet capable language model based on the Gemma 3 270M architecture. It has been fine-tuned using LoRA (Low-Rank Adaptation) adapters on a substantial instruction-following dataset, mlabonne/FineTome-100k, comprising around 100,000 samples. The fine-tuning process utilized the Unsloth framework for efficiency, completing in approximately 98 minutes on an NVIDIA RTX 5070 Ti.
Key Capabilities
- Instruction Following: Designed to accurately follow user instructions.
- Conversational AI: Suitable for developing chatbots and interactive agents.
- Question Answering: Can provide answers to queries based on its training.
- Text Generation: Capable of generating coherent and contextually relevant text.
Technical Details
- Base Model:
unsloth/gemma-3-270m-it - Parameters: 0.3 billion
- Context Length: 32768 tokens
- Training Data:
mlabonne/FineTome-100k(100k samples) - LoRA Configuration:
r=128,alpha=128, targetingq_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_projmodules. - Available Formats: Safetensors (for Transformers, vLLM) and GGUF (Q8_0, BF16 for
llama.cpp, Ollama).
Usage Considerations
This model is well-suited for applications requiring a small, efficient instruction-tuned model. However, it is explicitly noted as out-of-scope for generating medical or legal advice, creating harmful content, or production use without thorough evaluation.