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Model: opensynapselabs/arche3.5-codium-0.5B Source: Original Platform
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README.md
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README.md
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---
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tags:
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- code
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- code-generation
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- code-completion
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- code-llm
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- python
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- programming
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- developer-tools
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- instruct
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- finetuned
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- qwen
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- small-model
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- edge-device
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- local-ai
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- open-source
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- humaneval
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- function-calling
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- cli-tool
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- arche-code
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- text-generation
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- causal-lm
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- transformers
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- lightweight
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- cpu-friendly
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- apple-silicon
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- inference
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- autocomplete
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- llm
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- 500m
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- python-code
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license: apache-2.0
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language:
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- en
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- code
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spaces:
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- opensynapselabs/arche-codium-playground
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Arche-Codium-500M
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Compact, instruction-finetuned code generation model built on **Qwen2.5-Coder-0.5B-Instruct**. Fast local code completion with minimal resources.
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## TL;DR
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- **500M parameters** — runs on CPU, MPS, or low-VRAM GPU
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- **80% pass rate** on HumanEval (16/20 tasks)
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- **Apache 2.0** — fully open, commercially usable
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- **CLI-ready** — plug into [arche-code](https://github.com/OpenSynapseLabs/arche-code)
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## Live Demo
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Try the model directly in your browser — no setup required:
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[](https://huggingface.co/spaces/opensynapselabs/arche-codium-playground)
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## Quick Start
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### With arche-code CLI
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```bash
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git clone https://github.com/OpenSynapseLabs/arche-code.git
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cd arche-code
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pip install -e .
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arche --provider arche --model arche-codium-500m write "def fibonacci(n):" --max-tokens 256
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```
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The CLI auto-downloads the model on first use. Full docs: [github.com/OpenSynapseLabs/arche-code](https://github.com/OpenSynapseLabs/arche-code)
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### With transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"OpenSynapseLabs/arche-codium-500m",
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torch_dtype="auto", device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("OpenSynapseLabs/arche-codium-500m")
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prompt = '''def has_close_elements(numbers: list[float], threshold: float) -> bool:
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"""Check if any two numbers are closer than threshold."""'''
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.2)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Benchmarks
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| Benchmark | Result |
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|-----------|--------|
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| **HumanEval** | **16/20 (80%)** |
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## What This Model Is
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- **Lead magnet** — free, capable entry point into the Arche ecosystem
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- **Edge-friendly** — runs on laptops, Raspberry Pi, mobile devices
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- **Real code** — generates executable Python, not just snippets
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## What This Model Is Not
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- A replacement for 7B+ models on complex architecture tasks
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- A chat model — instruction-tuned for code generation only
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- The final word — larger Arche coding models are shipping this month
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## Model Details
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| Property | Value |
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|----------|-------|
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| Base model | Qwen2.5-Coder-0.5B-Instruct |
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| Parameters | 0.49B |
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| License | Apache 2.0 |
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| Training | Instruction fine-tuning on code-completion tasks |
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## Hardware Requirements
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| Device | VRAM/RAM | Speed |
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|--------|----------|-------|
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| Apple Silicon (MPS) | 2 GB unified | ~50 tok/s |
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| NVIDIA GPU (CUDA) | 2 GB | ~80 tok/s |
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| CPU only | 4 GB RAM | ~10 tok/s |
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## Limitations
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- Struggles with multi-step reasoning (e.g., LRU cache with TTL)
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- May truncate output at `max_tokens` limits — increase if code cuts off
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- Hallucinates imports occasionally — always verify generated code
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- Best for functions under 50 lines; breaks down on large classes
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## Citation
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```bibtex
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@software{arche_codium_500m,
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author = {Open Synapse Labs},
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title = {Arche-Codium-500M: Compact Code Generation Model},
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year = {2026},
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url = {https://huggingface.co/OpenSynapseLabs/arche-codium-500m}
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}
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```
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## Contact
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📧 opensynapselabs@proton.me
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🐙 [github.com/OpenSynapseLabs](https://github.com/OpenSynapseLabs)
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---
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*Built by Open Synapse Labs. Base model: Qwen2.5-Coder-0.5B-Instruct (Apache 2.0).*
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