DeepSeek-V3.2-Speciale-Distilled-Raptor-32B-4bit

Frontier Intelligence, Distilled and Optimized

DeepSeek-V3.2-Speciale-Distilled-Raptor-32B-4bit represents our approach to bringing frontier-level capabilities to on-premises deployment. We took DeepSeek V3.2 Speciale, a model with exceptional reasoning and code generation abilities, and distilled it into a 32 billion parameter model enhanced with our Raptor reasoning patterns. The result is a model that maintains frontier-level performance while fitting comfortably on consumer hardware through 4-bit quantization.

Distillation Methodology

Model distillation is not just about making things smaller. It is about transferring the capabilities of a large model into a more efficient package without losing what makes the original model valuable. We distilled DeepSeek V3.2 Speciale, preserving its sophisticated reasoning patterns, mathematical capabilities, and code generation expertise.

The distillation process involved training our 32B parameter model to match the outputs and internal representations of the larger DeepSeek model. We did not just train on the final outputs—we aligned intermediate layers, attention patterns, and activation distributions. This produces a model that thinks like the larger model, not just one that produces similar final answers.

We enhanced the distilled model with our Raptor reasoning patterns. This means structured logical deduction chains, systematic problem decomposition, and coherent multi-step thinking. The combination of DeepSeek's strong foundation and Raptor's reasoning methodology produces a model that excels at complex analytical tasks.

DeepSeek Heritage

DeepSeek models are known for exceptional mathematical reasoning and code generation. The V3.2 Speciale variant represents their most capable work in these domains. By distilling from this model, we inherit its strengths in algorithmic thinking, system design, and technical analysis.

The mathematical reasoning capabilities are substantial. The model handles calculus, linear algebra, probability, and advanced mathematics with accuracy that rivals much larger models. It can explain its reasoning steps, identify errors in mathematical arguments, and suggest alternative approaches to problems.

Code generation benefits from DeepSeek's training on vast amounts of high-quality code. The model understands language-specific idioms, design patterns, and best practices across multiple programming languages. It generates code that is not just syntactically correct but follows conventions and maintains readability.

Raptor Enhancement

Our Raptor reasoning patterns add structure to the model's analytical thinking. The model learns to decompose complex problems into manageable subproblems, tackle them systematically, and synthesize results into coherent solutions. This structured approach makes the model's reasoning transparent and debuggable.

Logical deduction chains become explicit. The model does not just jump to conclusions—it shows its work. This is valuable for technical analysis where understanding the reasoning path is as important as getting the right answer.

The enhancement also improves multi-step reasoning coherence. The model maintains context across extended reasoning chains, remembers intermediate results, and builds on previous conclusions without losing track of the original problem.

Architecture and Performance

Thirty-two billion parameters total, quantized to 4-bit precision. The 4-bit quantization reduces the effective parameter count to approximately 5 billion parameters worth of memory, bringing the footprint down to just 15GB. This makes the model deployable on standard laptops and even some high-end mobile devices. On M1 max, you are looking at good 120 tokens per second sustained throughput.

The 4-bit quantization is aggressive, but we have tested extensively to ensure it does not degrade the model's core capabilities. Mathematical reasoning remains accurate. Code generation maintains quality. The model does not hallucinate more or lose logical coherence. The trade-off is primarily in fine-grained language nuance rather than fundamental capability.

MLX-based inference leverages unified memory architecture for efficient deployment on Apple hardware. The model runs without requiring discrete GPUs or specialized hardware. Everything happens locally, with no network dependencies or external API calls.

What It Does Well

This model excels at complex system architecture and design. It can analyze large codebases, identify architectural patterns, suggest refactoring strategies, and explain the implications of different design choices. Algorithm design and optimization are core strengths—the model understands algorithmic complexity, can suggest more efficient approaches, and explains the trade-offs.

For research and analysis, the model handles scientific computing, mathematical problem-solving, and technical research with sophistication. It can work through research papers, implement algorithms from academic literature, and explain complex technical concepts clearly.

Data science and machine learning applications benefit from the model's understanding of statistical methods, optimization techniques, and modeling approaches. It can help design experiments, analyze results, and suggest improvements to modeling pipelines.

Running On-Premises

The model runs entirely on your hardware as part of Bodega OS. Complex system designs, proprietary algorithms, research insights—none of it leaves your machine. This is critical for enterprise applications where intellectual property and confidential information cannot be sent to external APIs.

Integration with Bodega's retrieval engines allows the model to search through your local codebase, technical documentation, and research materials. All retrieval and reasoning happens locally, maintaining complete privacy and control over your data.

Technical Decision Support

One of the model's strengths is technical decision support. When you are evaluating architectural choices, the model can analyze the implications of different approaches, identify potential issues, and suggest alternatives. It understands the engineering trade-offs between performance, maintainability, scalability, and development time.

Code review and analysis become systematic rather than ad-hoc. The model can identify code smells, suggest refactoring opportunities, detect potential bugs, and explain why certain patterns are problematic. This is not just pattern matching—the model understands the semantic meaning of code and can reason about correctness and performance.

For large-scale refactoring, the model can plan multi-step transformation strategies, identify dependencies that need to be updated, and suggest an order of operations that minimizes breakage. This kind of systematic planning is where the Raptor reasoning enhancement shows its value.

Part of the Bodega Ecosystem

This model represents the high end of our on-premises deployment options. For applications that need maximum reasoning capability running locally, this is the model to use. It pairs well with smaller models in the Raptor series for hybrid workflows—use lighter models for routine tasks and bring in the 32B model when you need serious analytical horsepower.

The model integrates seamlessly with Bodega OS, providing advanced reasoning capabilities for autonomous agents, technical decision-making, and complex problem-solving. It serves as a specialized reasoning engine that other components can call when they need deep analysis.

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Disclaimer

SRSWTI is not the creator or owner of the underlying foundation model architecture. The foundation model is created and provided by third parties. SRSWTI has trained this model on top of the foundation model but does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any outputs. You understand that this model can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. SRSWTI may not monitor or control all model outputs and cannot, and does not, take responsibility for any such outputs. SRSWTI disclaims all warranties or guarantees about the accuracy, reliability or benefits of this model. SRSWTI further disclaims any warranty that the model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to this model, your downloading of this model, or use of this model provided by or through SRSWTI.

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Crafted by the Bodega team at SRSWTI Research Labs
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Developed by SRSWTI Inc. - Building world's fastest retrieval and inference engines.