license, base_model, tags, language, library_name, pipeline_tag

license	base_model	tags	language	library_name	pipeline_tag
cc-by-nc-sa-4.0	Qwen/Qwen2.5-0.5B-Instruct	task-automation local-llm gguf cpu-inference fine-tuned	en	llama-cpp	text-generation

ACE 0.5B - Task Automation Model

Fine-tuned Qwen 0.5B for local task automation. Detects task types and generates execution plans.

Code: GitHub

Model Description

ACE is a 0.5B parameter language model fine-tuned for task automation. It can:

• Classify tasks (atomic, repetitive, clarification needed)
• Generate CLI commands for file operations
• Create execution plans with hotkeys
• Handle repetitive bulk operations

All inference runs on CPU - no GPU required.

Model Files

File	Size	Quant	Use Case
ace-bf16.gguf	940MB	BF16	Recommended - A bit slower inference, but better quality
ace-q4-k-m.gguf	385MB	Q4_K_M	Faster inference

Training Details

Base Model: Qwen/Qwen2-0.5B
Method: LoRA fine-tuning (r=16, alpha=32)
Dataset: ~1000 custom task examples
Training: 500-700 steps, learning_rate=3e-5
Quantization: GGUF Q4_K_M with imatrix

Task Types:

• Atomic tasks (single operations)
• Repetitive tasks (bulk processing)
• Clarification requests (ambiguous inputs)

Data Format:

Input: {"task": "...", "directory": [...], "available_hotkeys": [...]}
Output: {"task_type": "atomic", "output": {"execution_plan": {...}}}

Usage

• Right now the model is a bit unstable and intended for only experimental usages.
• Refer to the GitHub repo for installation and usage.

Limitations

• Requires explicit file paths (no smart file search)
• Optimized for Linux commands (Should be able to work on Windows)
• CPU inference only (3-10 seconds on i3/i5)
• No visual understanding (text-only)
• English language only

Performance

Hardware benchmarks:

• Intel i5 (2018+): 3-5 seconds per task
• Intel i3 (2015+): 5-10 seconds per task
• Older hardware: 30-90 seconds per task

Bias and Ethics

Known biases:

• Training data focused on common developer workflows
• Linux command bias (more Linux than Windows examples)
• English-only (no multilingual support)

Ethical considerations:

• Model can generate destructive commands (file deletion)
• Users should review plans before execution
• No built-in safety checks for harmful operations

License

CC BY-NC-SA 4.0 (Non-commercial)

• ✅ Free for personal/research use
• ❌ Commercial use requires separate license
• ✅ Must provide attribution
• ✅ Derivatives must use same license

Additional Restriction: Training of AI/ML models using these weights is prohibited without explicit written permission.

Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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More info: GitHub Repository