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README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
 #### Model Details
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 <b>Nerdsking-python-coder-3B-i</b> is a 3B parameter partially uncensored model focused in <b> Python</b>, with <b>English</b> as main language. It was massively trained in python, therefore despite the fact it can code in other languages as well, the performance will be not in the same level as the one achieved while using python.
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 <i>Key Characteristics:</i>
 
 - Parameter count: 3B
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 #### Benchmark
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 After intense refining, <b>Nerdsking-python-coder-3B-i</b> has achieved <b>88.41 in HumanEval (bf16)</b>, ranking it amongst the highest-performing Python-focused 3B models ever reported on HumanEval. Surpassing even much bigger models in that area. 
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 <i>Benchmark details (164 tasks):</i>
 
 - official HumanEval execution protocol - test suites executed via `exec()`
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 - evaluated on fully merged weights
 - Prompting: Chat-formatted with a fixed system prompt (“You are an expert Python coding assistant.”)
 - Quantization: None (unquantized weights - bf16)
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 <i>The configuration above is fully disclosed to support reproducibility and fair comparison.</i>
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 <i> Note: Quantized variants (INT4/INT6) may exhibit lower HumanEval scores due to reduced numerical precision.</i>
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 #### Comparison Table
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   <em>*Estimated/proxy values where standardized HumanEval pass@1 was not published in those 3 models. Scores can vary with prompt format, decoding params, and harness.</em>
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 #### S.o.n.n.
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 The model was treated under <b>"s.o.n.n."</b> (<i>single omni neural network</i>), a concept created by IPMN at Nerdsking.com that is both a precise way of fine tunning/altering existing models, as well a foundational concept for a broader AI architecture standard currently under active research and development.
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 <i>When applied to pre-existing models, allows:</i>
 
 - parameter-preserving refinement methodology
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 #### Ethical & Safety Notes
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 This model is intended for technical and research use.
 Due to relaxed alignment constraints, outputs should be reviewed before deployment in production or public-facing systems.
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 #### Citation