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🔗 SHA sum: 6d370b86518ab84cc570634e548913e7 | Updated: 2026-07-20



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Viewing a PDF file with annotations and form fields filled in.

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  • Processor: next-gen chip for heavy context processing
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Storage: extra room for future model updates and datasets
  • Graphics: 12 GB VRAM minimum required for basic quantization

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Key Features and Benchmarks

• **Reasoning**: Outperforms existing open-source models in reasoning tasks• **Coding**: Exhibits exceptional coding capabilities, making it a valuable tool for developers• **Language Understanding**: Demonstrates superior language understanding skills

Benchmark Comparison Results
Reasoning Task Outperformed existing models by 25%
Coding Challenge Completed coding tasks with 99.9% accuracy
Language Understanding Test Achieved a 95% accuracy rate in language understanding

Di-IMatrix Optimization Layer: The Key to Reduced Memory Footprint

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Technical Specifications

Value
Parameters 40 B
Context Length 8 K tokens
Training Data ≈1.5 trillion tokens
Inference Speed ≈200 tokens/s (GPU)
Quantization GGUF (Q4_K_M)

Potential Applications and Future Directions

The Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF model’s capabilities make it an attractive solution for various applications, including research and education. Its uncensored thinking mode encourages transparent reasoning steps, making it especially valuable in these domains.

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📄 Hash Value: b41bcb9d00c228bb3f7a6c3fa88fde44 | 📆 Update: 2026-07-16



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Gemma-4-31B-it-qat-w4a16-ct: Unveiling the Large Language Model’s Potential

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31 B
Quantization QAT (w4a16)
Precision 16-bit float
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🧾 Hash-sum — 60ee7c7ee58814194e4d2dd4fdd8b899 • 🗓 Updated on: 2026-07-12



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