Qwen3

Qwen3 GGUF (4_K_M Quantized)

This repository hosts GGUF-format quantized versions of Qwen3 models at multiple parameter sizes.
All models are quantized at 4_K_M, selected to provide a practical balance of inference performance, memory usage, and output quality.

These files are intended for use with SciTools’ Understand and Onboard, as well as other tools and runtimes that support the GGUF format (for example, llama.cpp-based applications).

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Model Details

• Base models: Qwen3 (various parameter sizes)
• Format: GGUF
• Quantization: 4_K_M
• Intended use: Local inference, code understanding, general-purpose chat
• Languages: Multilingual (as supported by Qwen3)

Available Variants

This repository includes multiple Qwen3 parameter sizes, each quantized independently but consistently using the same 4_K_M scheme. Refer to the file names for exact parameter counts.

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Quantization Process

• All models are quantized using the 4_K_M quantization method.
• Quantization was performed directly by the Qwen team where available.
• In a small number of cases, quantization was performed by Unsloth.
• No further modifications, rebalancing, or fine-tuning were applied.
• The quantization parameters and defaults were not altered from the original sources.

The goal is to provide faithful, reproducible GGUF variants that behave as closely as possible to their upstream counterparts within the constraints of 4-bit quantization.

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What We Did Not Do

To be explicit:

• No additional fine-tuning
• No instruction rebalancing
• No safety, alignment, or prompt modifications
• No merging or model surgery

If a model behaves a certain way, that behavior comes from Qwen3 combined with 4_K_M quantization, not from any downstream changes here.

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Intended Use

These models are suitable for:

• SciTools Understand and SciTools Onboard
• Local AI workflows
• Code comprehension and exploration
• Interactive chat and analysis
• Integration into developer tools that support GGUF

They are not intended for:

• Safety-critical or regulated decision-making
• Use cases requiring guaranteed factual accuracy
• Production deployment without independent evaluation

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Limitations

• As 4-bit quantized models, some degradation in reasoning depth and numerical precision is expected compared to full-precision checkpoints.
• Output quality varies by parameter size and task.
• Like all large language models, Qwen3 may produce hallucinations or incorrect information.

Evaluate carefully for your specific workload.

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License & Attribution

• Original models: Qwen / Alibaba Cloud
• Quantization: Qwen and Unsloth
• Format: GGUF (llama.cpp ecosystem)

Please refer to the original Qwen3 license and usage terms. This repository redistributes quantized artifacts only and does not change the underlying licensing conditions.

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Acknowledgements

Thanks to the Qwen team for releasing Qwen3 models and to Unsloth for high-quality, reproducible quantization tooling that enables efficient local inference across a wide range of tools.