🌸 PyBlissa-Coder-40M

!! 14.6% SCORE ON HumanEval PASS@1 !!

PyBlissa-Coder-40M is the second model from the PyBlissa-Coder family that mainly supports python coding. Despite its small footprint, 40M parameters, trained on 272M tokens, PyBlissa has achieved an amazing score of 14.6% on the HumanEval dataset; and 4.4% on MBPP dataset (both being benchmark datasets). While scoring such a good number, it's imperfections are also something to be aware of. This model can sometimes generate wrong, inefficient, or broken codes. Though its mostly temperature-dependent.

Benchmarks

Benchmark	Score	Protocol	Temp
HumanEval pass@1	14.6% (24/164)	zero-shot, fenced-code extraction	0.25
MBPP pass@1	4.4% (22/500)	official tests-in-prompt (Austin et al. 2021)	0.05

How PyBlissa compares on HumanEval

Model	Params	HumanEval pass@1
GPT-Neo	125M	0.75%
CodeParrot-small	110M	3.80%
PyCodeGPT	110M	8.33%
PyBlissa-Coder	40M	14.6%

PyBlissa is ~2.75× smaller than CodeParrot-small yet scores roughly 4× higher on HumanEval pass@1, trained on a single consumer GPU.

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Model details

Architecture	Decoder-only transformer (GPT-2 style, nanoGPT lineage)
Parameters	39.9M
Layers	10
Model dim (d_model)	512
Heads	8 (head_dim 64)
FFN dim (d_ff)	2048
Context length	512 tokens
Vocab size	16,000 (custom ByteLevel BPE)
Tied embeddings	Yes
Precision	trained in bf16, released as F32 GGUF
Best val loss	0.3615

Training

Hardware	1 × NVIDIA RTX 5080 (16 GB)
Training tokens	272M (train split)
Epochs	5
Optimizer	AdamW (β 0.9/0.95, wd 0.1)
LR schedule	cosine, 4e-4 → 4e-5, ~2% warmup
Batch size	48
Total steps	55,405
Wall-clock time	~116 min

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Usage

Ollama

ollama run hf.co/Rohanify/PyBlissa-Coder-40M:F32

The repo ships template and params files, so Ollama applies the correct PROMPT:/CODE: format and sampling defaults automatically — no Modelfile needed for remote runs.

To run a local GGUF instead:

ollama create pyblissa-40m -f Modelfile
ollama run pyblissa-40m "write a function that checks if a number is prime"

Prompt format

The model was trained on a plain-text wrapper. At inference, the prompt is wrapped as:

PROMPT: {your instruction}
CODE:

The model then emits a fenced ```python code block. (When using Ollama, the template file does this wrapping for you — just type a plain instruction.)

Recommended sampling

Parameter	Value
temperature	0.25 – 0.3
top_k	10
repeat_penalty	1.25
num_ctx	512

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Limitations

PyBlissa is a 40M-parameter model trained primarily for prompt → Python generation. Known limitations:

• It is a small model: it solves short, self-contained functions well but struggles with multi-step or library-heavy tasks.
• It sometimes omits import statements for stdlib modules it uses (math, re, hashlib, etc.).
• It can occasionally emit a short natural-language preamble before the code block on harder prompts.
• Code explanation and non-Python tasks are out of distribution — it may attempt them, but that is not what it was trained for.
• As with any code model, review and test generated code before running it.

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Training data & attribution

This model was trained on the following datasets. Per their licenses, attribution is provided here:

• nvidia/OpenCodeInstruct — CC-BY-4.0 https://huggingface.co/datasets/nvidia/OpenCodeInstruct
• flytech/python-codes-25k — MIT https://huggingface.co/datasets/flytech/python-codes-25k

No OpenAI-derived data was used in training.

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License

The model weights are released under Apache-2.0. Note that the training data carries its own licenses (CC-BY-4.0 and MIT, see above), which require attribution as provided.

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@misc{pyblissa2026,
  title  = {PyBlissa-Coder-40M: A from-scratch Python code model},
  author = {Rohan},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/Rohanify/PyBlissa-Coder-40M}}
}