Instructions to use bytesbrains/naderu-geek-py-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bytesbrains/naderu-geek-py-0.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bytesbrains/naderu-geek-py-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bytesbrains/naderu-geek-py-0.5b")
model = AutoModelForCausalLM.from_pretrained("bytesbrains/naderu-geek-py-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bytesbrains/naderu-geek-py-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bytesbrains/naderu-geek-py-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytesbrains/naderu-geek-py-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bytesbrains/naderu-geek-py-0.5b

SGLang

How to use bytesbrains/naderu-geek-py-0.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bytesbrains/naderu-geek-py-0.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytesbrains/naderu-geek-py-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bytesbrains/naderu-geek-py-0.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytesbrains/naderu-geek-py-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bytesbrains/naderu-geek-py-0.5b with Docker Model Runner:
```
docker model run hf.co/bytesbrains/naderu-geek-py-0.5b
```

naderu-geek-py-0.5b

A small coding assistant by Naderu — 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 (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

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

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

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 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.

Citation / attribution

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

@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}
}

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Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for bytesbrains/naderu-geek-py-0.5b

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Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B-Instruct

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Adapters

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naderu-geek-py-0.5b