README.md

---
license: apache-2.0
language:
- en
tags:
- code
- python
- text-generation
- gpt2
- from-scratch
- small-model
- code-generation
pipeline_tag: text-generation

---
# 🌸 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.


<p align="center">
  <img src="pyblissa_banner.png" alt="PyBlissa-Coder-40M" width="100%">
</p>

<p align="center">
  <img src="pyblissa_loss_curve.png" alt="Training curve" width="100%">
</p>


## 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.
---

## 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 |

---

## Usage

### Ollama

```bash
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:

```bash
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 |

---

## 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.**

---

## 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.

---

## 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.

---

```bibtex
@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}}
}
```