arche3.5-codium-0.5B/README.md

tags, license, language, spaces, base_model, pipeline_tag, library_name

tags	license	language	spaces	base_model	pipeline_tag	library_name
code code-generation code-completion code-llm python programming developer-tools instruct finetuned qwen small-model edge-device local-ai open-source humaneval function-calling cli-tool arche-code text-generation causal-lm transformers lightweight cpu-friendly apple-silicon inference autocomplete llm 500m python-code	apache-2.0	en code	opensynapselabs/arche-codium-playground	Qwen/Qwen2.5-0.5B-Instruct	text-generation	transformers

Arche-Codium-500M

Compact, instruction-finetuned code generation model built on Qwen2.5-Coder-0.5B-Instruct. Fast local code completion with minimal resources.

TL;DR

• 500M parameters — runs on CPU, MPS, or low-VRAM GPU
• 80% pass rate on HumanEval (16/20 tasks)
• Apache 2.0 — fully open, commercially usable
• CLI-ready — plug into arche-code

Live Demo

Try the model directly in your browser — no setup required:

Quick Start

With arche-code CLI

git clone https://github.com/OpenSynapseLabs/arche-code.git
cd arche-code
pip install -e .
arche --provider arche --model arche-codium-500m write "def fibonacci(n):" --max-tokens 256

The CLI auto-downloads the model on first use. Full docs: github.com/OpenSynapseLabs/arche-code

With transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OpenSynapseLabs/arche-codium-500m",
    torch_dtype="auto", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("OpenSynapseLabs/arche-codium-500m")

prompt = '''def has_close_elements(numbers: list[float], threshold: float) -> bool:
    """Check if any two numbers are closer than threshold."""'''

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmarks

Benchmark	Result
HumanEval	16/20 (80%)

What This Model Is

• Lead magnet — free, capable entry point into the Arche ecosystem
• Edge-friendly — runs on laptops, Raspberry Pi, mobile devices
• Real code — generates executable Python, not just snippets

What This Model Is Not

• A replacement for 7B+ models on complex architecture tasks
• A chat model — instruction-tuned for code generation only
• The final word — larger Arche coding models are shipping this month

Model Details

Property	Value
Base model	Qwen2.5-Coder-0.5B-Instruct
Parameters	0.49B
License	Apache 2.0
Training	Instruction fine-tuning on code-completion tasks

Hardware Requirements

Device	VRAM/RAM	Speed
Apple Silicon (MPS)	2 GB unified	~50 tok/s
NVIDIA GPU (CUDA)	2 GB	~80 tok/s
CPU only	4 GB RAM	~10 tok/s

Limitations

• Struggles with multi-step reasoning (e.g., LRU cache with TTL)
• May truncate output at max_tokens limits — increase if code cuts off
• Hallucinates imports occasionally — always verify generated code
• Best for functions under 50 lines; breaks down on large classes

Citation

@software{arche_codium_500m,
  author = {Open Synapse Labs},
  title = {Arche-Codium-500M: Compact Code Generation Model},
  year = {2026},
  url = {https://huggingface.co/OpenSynapseLabs/arche-codium-500m}
}

Contact

📧 opensynapselabs@proton.me
🐙 github.com/OpenSynapseLabs

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Built by Open Synapse Labs. Base model: Qwen2.5-Coder-0.5B-Instruct (Apache 2.0).