Nerdsking-python-coder-7B-i is a 7B parameter partially uncensored model focused in coding. 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.
Key Characteristics:
Parameter count: 7B
Primary domain: Python programming
Secondary capabilities: General coding, technical English
Training focus: Python logic, standard library usage, algorithmic reasoning
After intense refining, Nerdsking-python-coder-7B-i has achieved 86.99 in HumanEval (bf16), ranking it amongst the highest-performing Python-focused 7B models ever reported on HumanEval. Surpassing even much bigger models in that area.
Benchmark details (164 tasks):
official HumanEval execution protocol - test suites executed via exec()
zero-shot pass@1
dtype == "bfloat16"
temperature = 0.1
do_sample = False
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)
The configuration above is fully disclosed to support reproducibility and fair comparison.
Note: Quantized variants (INT4/INT6) may exhibit lower HumanEval scores due to reduced numerical precision.
Comparison Table
Model name
Approx. HumanEval Pass@1 (%)
Notes / Source
Nerdsking-python-coder-7B-i
86.99
Evaluated score (zero-shot, strict HumanEval pass@1, using unquantized weigths bf16)
Qwen2.5-Coder-7B
~74–76
Community evaluation (OpenCompass run); figures vary by harness/settings
DeepSeek-Coder-6.7B
~72–73
Official DeepSeek report and independent replications; close to strict HumanEval protocol
CodeLlama-7B
~33–35
Meta technical report
Wizard Coder 7B*
~57–59
Community benchmarks; strong instruction-following but less consistent zero-shot behavior
The model was treated under "s.o.n.n." (singular omni neural network), 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.
When applied to pre-existing models, allows:
parameter-preserving refinement methodology
focused global behavioral shaping, instead of task-local adapters
avoidance of fragmentation, very common in multi-adapter or task-siloed approaches
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.
Citation
If you use this model in research and/or benchmarking, please cite:
