Instructions to use Rohanify/PyBlissa-Coder-40M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rohanify/PyBlissa-Coder-40M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rohanify/PyBlissa-Coder-40M",
	filename="PyBlissa-V2-F32.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Rohanify/PyBlissa-Coder-40M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rohanify/PyBlissa-Coder-40M:F32
# Run inference directly in the terminal:
llama-cli -hf Rohanify/PyBlissa-Coder-40M:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rohanify/PyBlissa-Coder-40M:F32
# Run inference directly in the terminal:
llama-cli -hf Rohanify/PyBlissa-Coder-40M:F32

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rohanify/PyBlissa-Coder-40M:F32
# Run inference directly in the terminal:
./llama-cli -hf Rohanify/PyBlissa-Coder-40M:F32

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rohanify/PyBlissa-Coder-40M:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rohanify/PyBlissa-Coder-40M:F32

Use Docker

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

LM Studio
Jan

vLLM

How to use Rohanify/PyBlissa-Coder-40M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rohanify/PyBlissa-Coder-40M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rohanify/PyBlissa-Coder-40M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

Ollama
How to use Rohanify/PyBlissa-Coder-40M with Ollama:
```
ollama run hf.co/Rohanify/PyBlissa-Coder-40M:F32
```

Unsloth Studio

How to use Rohanify/PyBlissa-Coder-40M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rohanify/PyBlissa-Coder-40M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rohanify/PyBlissa-Coder-40M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rohanify/PyBlissa-Coder-40M to start chatting

Atomic Chat new
Docker Model Runner
How to use Rohanify/PyBlissa-Coder-40M with Docker Model Runner:
```
docker model run hf.co/Rohanify/PyBlissa-Coder-40M:F32
```

Lemonade

How to use Rohanify/PyBlissa-Coder-40M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rohanify/PyBlissa-Coder-40M:F32

Run and chat with the model

lemonade run user.PyBlissa-Coder-40M-F32

List all available models

lemonade list

🌸 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.

PyBlissa-Coder-40M

Training curve

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

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

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.

@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}}
}

Downloads last month: -

GGUF

Model size

40M params

Architecture

gpt2

Hardware compatibility

32-bit