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Model: opensynapselabs/arche3.5-codium-0.5B Source: Original Platform
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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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config.json
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|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"max_length": 768,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|im_end|>",
|
||||
"split_special_tokens": false,
|
||||
"stride": 0,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"truncation_side": "right",
|
||||
"truncation_strategy": "longest_first",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user