Open4bits/gemma-3-270m-it-gguf

gemma-3-270m-gguf

This repository contains GGUF-formatted, quantized versions of Gemma 3 270M (base model), prepared and published by Open4Bits for efficient local inference on low-resource systems.

Open4Bits is a model distribution and optimization initiative under ArkAI Labs, which is run and operated by ArkDevLabs. Through Open4Bits, ArkAI Labs publishes quantized, optimized, and deployment-ready models in GGUF and other inference-friendly formats.

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About Open4Bits

Open4Bits focuses on making modern language models usable on real-world hardware.

Through Open4Bits, ArkAI Labs publishes:

• Quantized language models
• GGUF models for local inference
• Optimized formats for CPU-friendly deployment
• Lightweight variants for low-resource systems

The goal is to enable practical AI usage without requiring high-end GPUs.

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Available Models

File	Quantization	Notes
gemma-3-270m-IQ4_NL.gguf	IQ4_NL	Ultra-light, fastest, lowest memory usage
gemma-3-270m-IQ4_XS.gguf	IQ4_XS	Slightly higher quality than IQ4_NL
gemma-3-270m-Q4_0.gguf	Q4_0	Legacy 4-bit quantization
gemma-3-270m-Q4_1.gguf	Q4_1	Legacy 4-bit with improved accuracy
gemma-3-270m-Q4_K_S.gguf	Q4_K_S	Modern K-quant (small)
gemma-3-270m-Q4_K_M.gguf	Q4_K_M	Modern K-quant (medium, recommended)
gemma-3-270m-Q5_0.gguf	Q5_0	Legacy 5-bit quantization
gemma-3-270m-Q5_1.gguf	Q5_1	Legacy 5-bit with improved accuracy
gemma-3-270m-Q5_K_S.gguf	Q5_K_S	Modern K-quant (small, higher quality)
gemma-3-270m-Q5_K_M.gguf	Q5_K_M	Modern K-quant (medium, high quality)
gemma-3-270m-Q8_0.gguf	Q8_0	Near-baseline quality (reference)

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Recommended Variants

• Q4_K_M – Recommended for most users (best balance of quality, speed, and memory)
• Q5_K_M – Higher quality for users who can afford additional memory usage
• Q8_0 – Near-baseline reference for evaluation and comparison

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Quantization Overview

IQ4 Variants

• Extremely small and fast
• Suitable for very limited hardware
• Noticeable quality reduction compared to K-quant variants

Q4_0 / Q4_1

• Older quantization formats
• Included for compatibility and comparison
• Generally inferior to modern K-quant variants

K-Quant Variants (Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M)

• Newer, higher-quality quantization methods
• Better accuracy per bit
• Standard choice for GGUF deployments

Q8_0

• Near-baseline (FP16-like) quality
• Highest memory usage among provided variants
• Intended for reference, testing, and debugging

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Usage with llama.cpp

./main -m gemma-3-270m-Q4_K_M.gguf \
  -p "Explain what GGUF quantization is." \
  -n 128

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Usage with Ollama

Create a Modelfile:

FROM ./gemma-3-270m-Q4_K_M.gguf

Then run:

ollama create gemma-270m -f Modelfile
ollama run gemma-270m

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System Requirements (Approximate)

Quantization	Memory Requirement
IQ4_NL / IQ4_XS	~300–400 MB
Q4_0 / Q4_1	~450–500 MB
Q4_K_S	~500 MB
Q4_K_M	~550–600 MB
Q5_K_S	~600–650 MB
Q5_K_M	~650–750 MB
Q8_0	~900 MB – 1 GB

Actual memory usage depends on context length and runtime configuration.

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Intended Use and Limitations

This model is a small, lightweight base language model optimized for low-resource inference.

It is suitable for:

• Token generation and experimentation
• Embedding-style or downstream fine-tuning use
• Educational and demonstration purposes
• Lightweight local inference

Limitations:

• Not instruction-tuned
• Not designed for complex reasoning tasks
• Not suitable for advanced code generation
• May hallucinate or produce incomplete outputs for complex prompts

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License

This repository follows the original Gemma model license.

Users are responsible for complying with all upstream license terms and usage restrictions.

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Credits

• Original model: Google (Gemma)
• Quantization and GGUF packaging: Open4Bits (ArkAI Labs, operated by ArkDevLabs)
• Tooling: llama.cpp

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Support

Open4Bits is an initiative under ArkAI Labs, operated by ArkDevLabs, focused on open and accessible AI.

If you find these models useful, please consider supporting the work by:

• Liking the model on Hugging Face
• Sharing feedback or reporting issues
• Using Open4Bits models in your projects and research

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Organization Links

• Open4Bits on Hugging Face: https://huggingface.co/Open4Bits
• ArkAI Labs on Hugging Face: https://huggingface.co/ArkAiLab-Adl
• ArkDevLabs Website: https://arkdevlabs.com