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geoffmunn/Qwen3-30B-A3B-Thinking-2507

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
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Qwen3-30B-A3B-Thinking-2507-GGUF

This is a GGUF-quantized version of the [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) language model - a 30-billion-parameter thinking model with advanced reasoning capabilities, chain-of-thought processing, and state-of-the-art performance for complex problem-solving tasks. Converted for use with llama.cpp, LM Studio, OpenWebUI, GPT4All, and more.

💡 Key Features of Qwen3-30B-A3B-Thinking-2507:

  • —🤔 Advanced thinking mode with chain-of-thought reasoning for complex math, coding, and logical problem-solving.
  • —🔁 Dynamically switch via /think and /no_think in conversation for step-by-step problem solving.
  • —🧠 State-of-the-art reasoning - ideal for research, complex analysis, and professional applications requiring deep thinking.
  • —🧰 Agent-ready: integrates seamlessly with tools via Qwen-Agent or MCP for autonomous workflows.
  • —🌍 Fluent in 100+ languages including Chinese, English, Arabic, Japanese, Spanish, and more.
  • —🏆 Enterprise-grade performance for professional and academic use cases requiring maximum accuracy.
  • —💼 Research-ready for advanced research, complex mathematics, and scientific applications.

💡 Why f32?

This model uses FP32 (32-bit floating point) as its base precision. This is unusual for GGUF models because:

  • —FP32 doubles memory usage vs FP16.
  • —Modern LLMs (including Qwen3) are trained in mixed precision and do not benefit from FP32 at inference time.
  • —Only useful for debugging, research, or extreme numerical robustness.
  • —For thinking models, FP32 may provide slightly better numerical stability in reasoning chains.

⚠️ Consider converting from 32 → 16 first using llama-convert if you control the source and want to reduce memory usage.

Available Quantizations (from f32)

LevelQualitySpeedSizeRecommendation
Q2_KMinimal⚡ Fast11.3 GBOnly on severely memory-constrained systems.
Q3KSLow-Medium⚡ Fast13.3 GBMinimal viability; avoid unless space-limited.
Q3KMLow-Medium⚡ Fast14.7 GBAcceptable for basic interaction.
Q4KSPractical⚡ Fast17.5 GBGood balance for mobile/embedded platforms.
Q4KMPractical⚡ Fast18.6 GBBest overall choice for most users.
Q5KSMax Reasoning🐢 Medium21.1 GBSlight quality gain; good for testing.
Q5KMMax Reasoning🐢 Medium21.7 GBBest quality available. Recommended.
Q6_KNear-FP16🐌 Slow25.1 GBDiminishing returns. Only if RAM allows.
Q8_0Lossless*🐌 Slow32.5 GBMaximum fidelity. Ideal for archival.
💡 Recommendations by Use Case
  • —🧠 Advanced Thinking & Reasoning: Q5KM or Q6_K for maximum thinking quality
  • —🔬 Research & Complex Analysis: Q6K or Q8K_XL for state-of-the-art reasoning
  • —💼 Enterprise Workstations (64GB+ RAM): Q5KM or Q6_K for professional use
  • —🤔 Thinking Mode Applications: Q5KM recommended for optimal thinking chain quality
  • —🛠️ Development & Testing: Test from Q4KM up to Q8KXL based on hardware
  • —⚠️ Note: Requires substantial RAM (32GB+ recommended for Q5KM+). Thinking models benefit from higher precision.

Usage

Load this model using:

  • —OpenWebUI - self-hosted AI interface with RAG & tools
  • —LM Studio - desktop app with GPU support
  • —GPT4All - private, offline AI chatbot
  • —Or directly via llama.cpp

Each quantized model includes its own README.md and shares a common MODELFILE.

Author

👤 Geoff Munn (@geoffmunn) 🔗 Hugging Face Profile

Disclaimer

This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.