Instructions to use opensynapselabs/arche3.5-codium-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opensynapselabs/arche3.5-codium-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="opensynapselabs/arche3.5-codium-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opensynapselabs/arche3.5-codium-0.5B")
model = AutoModelForCausalLM.from_pretrained("opensynapselabs/arche3.5-codium-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 opensynapselabs/arche3.5-codium-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opensynapselabs/arche3.5-codium-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": "opensynapselabs/arche3.5-codium-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/opensynapselabs/arche3.5-codium-0.5B

SGLang

How to use opensynapselabs/arche3.5-codium-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 "opensynapselabs/arche3.5-codium-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": "opensynapselabs/arche3.5-codium-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 "opensynapselabs/arche3.5-codium-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": "opensynapselabs/arche3.5-codium-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use opensynapselabs/arche3.5-codium-0.5B with Docker Model Runner:
```
docker model run hf.co/opensynapselabs/arche3.5-codium-0.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

Downloads last month: 50

Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for opensynapselabs/arche3.5-codium-0.5B

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct