---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - code
  - python
  - coding-assistant
  - lora
  - naderu
  - qwen2
---

# naderu-geek-py-0.5b

**A small coding assistant by [Naderu](https://naderu.com) — a BytesBrains Pte. Ltd. venture.**

`naderu-geek` is a compact, specialised coding model. It is a **portfolio / demonstration**
piece: a LoRA fine-tune of an open coding base that gives the model a recognisable
`naderu-geek` identity and a clean, commented Python style. It demonstrates Naderu's
model-engineering pipeline — data → training → evaluation → release — end to end, on a
deliberately small footprint.

> **Status: released (v0.1.0).** Trained 2026-07-14 (LoRA fine-tune, merged) and passed its
> qualitative smoke eval — see **Evaluation** below. Nothing here is a capability claim — see
> **Limitations**.

> This is an honest demonstration piece, **not** a state-of-the-art model. Its coding
> ability is essentially that of its base; the fine-tune adds identity and style, not new
> capability. See **Limitations**.

## Provenance

- **Fine-tuned from:** [`Qwen/Qwen2.5-Coder-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) (Apache-2.0)
- **Method:** LoRA (r=16, α=32; ~8.8M trainable params, 1.75%) on attention + MLP projections, adapter merged into the base
- **Training data:** a small, Naderu-authored instruction set (39 examples: identity + idiomatic Python) — license-clean, included in our repo
- **Precision:** trained in fp32; merged weights distributed in **bf16** (matching the base's format)
- **License:** Apache-2.0 (inherits the base model's terms)

## Intended use

- A lightweight Python coding helper: small functions, snippets, explanations.
- A reference example of a Naderu specialised-model release (card + provenance + eval).

**Out of scope:** production code generation at scale, non-Python languages, security-
sensitive code, or any use where correctness must be guaranteed without review.

## How to use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "bytesbrains/naderu-geek-py-0.5b"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are naderu-geek, a focused coding assistant by Naderu (naderu.com). You write clean, correct, well-commented code and explain briefly."},
    {"role": "user", "content": "Write a Python function to check if a number is prime."},
]
enc = tok.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
)
out = model.generate(**enc, max_new_tokens=200, do_sample=False)
print(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True))
```

The model is chat-templated (Qwen2 template). The system prompt above is the one it was
trained with; keeping it yields the most on-style responses.

## Evaluation

<!-- EVAL:START -->
**Suite:** `eval/suites/naderu-geek/run_eval.py` (qualitative smoke, greedy decode) ·
**Run:** 2026-07-14 · **Result: PASS**

| Check | Outcome |
|-------|---------|
| Identity recognised (`Who are you?` → names `naderu-geek`) | ✅ yes |
| Coherent, runnable Python (3 in-suite coding prompts) | ✅ 3/3 |

Observations (honest):

- **Identity imprinted and robust.** It reliably self-identifies as `naderu-geek` by Naderu,
  and on a held-out prompt (*"Are you ChatGPT or made by OpenAI?"*) it correctly denies and
  states its open-base provenance — i.e. the identity generalises beyond the exact training phrasings.
- **Clean Python style generalises.** On held-out tasks not in the training set (mean of a list,
  nth triangular number) it produced correct, type-hinted, docstring'd functions in the same
  house style — so the fine-tune transfers *style*, not just memorised answers.
- **In line with the base's capability.** In-suite answers closely track the curated exemplars
  (a 39-example set, low final train loss). This is the expected behaviour of a small identity/
  style imprint and **not** a capability claim — see **Limitations**.

Reproduce: `python training/train_lora.py && NG_MODEL=./naderu-geek-py-0.5b python eval/suites/naderu-geek/run_eval.py`
<!-- EVAL:END -->

This is a qualitative smoke check (identity recognised + coherent Python), not a
leaderboard score. For real releases, Naderu attaches reproducible benchmark results
tied to a versioned eval suite.

## Limitations & risks

- Tiny base (0.5B) + tiny fine-tune → limited reasoning; can produce incorrect or
  insecure code. **Always review generated code.**
- The fine-tune changes identity/style far more than capability.
- English + Python focus; other languages are best-effort from the base.

## About Naderu

[**Naderu**](https://naderu.com) is an AI-models company — a venture of **BytesBrains Pte. Ltd.**
We do three things, and only these:

- **Train** — turn foundation models into specialised ones (fine-tune, LoRA, distill, quantize).
- **Release** — publish specialised models with model cards, provenance, and clear licensing.
- **Serve** — the engineering around models (customization, deployment, operations).

Every Naderu model states its upstream foundation model and licence plainly, and every capability
claim is backed by a real evaluation — never vibes. `naderu-geek-py-0.5b` is our **first public
portfolio release**: small on purpose, honest about its scope, and reproducible end to end.

## Links

- 🌐 Website — [naderu.com](https://naderu.com)
- 🧩 Model family — `naderu-geek` (compact, specialised coding models)
- 🧱 Base model — [`Qwen/Qwen2.5-Coder-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
- 🪪 License — [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
- 🏷️ Version — v0.1.0 (released 2026-07-14)

## Citation / attribution

Built on Qwen2.5-Coder (Apache-2.0). Fine-tuned and released by **Naderu** (BytesBrains Pte. Ltd.).

```bibtex
@misc{naderu_geek_py_0_5b_2026,
  title        = {naderu-geek-py-0.5b},
  author       = {Naderu (BytesBrains Pte. Ltd.)},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/bytesbrains/naderu-geek-py-0.5b}},
  note         = {LoRA fine-tune of Qwen/Qwen2.5-Coder-0.5B-Instruct, Apache-2.0}
}
```