Instructions to use ansh0x/ace-0.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ansh0x/ace-0.5b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ansh0x/ace-0.5b-gguf",
	filename="ace-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ansh0x/ace-0.5b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ansh0x/ace-0.5b-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf ansh0x/ace-0.5b-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ansh0x/ace-0.5b-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf ansh0x/ace-0.5b-gguf:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ansh0x/ace-0.5b-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf ansh0x/ace-0.5b-gguf:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ansh0x/ace-0.5b-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ansh0x/ace-0.5b-gguf:BF16

Use Docker

docker model run hf.co/ansh0x/ace-0.5b-gguf:BF16

LM Studio
Jan

vLLM

How to use ansh0x/ace-0.5b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ansh0x/ace-0.5b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ansh0x/ace-0.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ansh0x/ace-0.5b-gguf:BF16

Ollama
How to use ansh0x/ace-0.5b-gguf with Ollama:
```
ollama run hf.co/ansh0x/ace-0.5b-gguf:BF16
```

Unsloth Studio new

How to use ansh0x/ace-0.5b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ansh0x/ace-0.5b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ansh0x/ace-0.5b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ansh0x/ace-0.5b-gguf to start chatting

Pi new

How to use ansh0x/ace-0.5b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ansh0x/ace-0.5b-gguf:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ansh0x/ace-0.5b-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ansh0x/ace-0.5b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ansh0x/ace-0.5b-gguf:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ansh0x/ace-0.5b-gguf:BF16

Run Hermes

hermes

Docker Model Runner
How to use ansh0x/ace-0.5b-gguf with Docker Model Runner:
```
docker model run hf.co/ansh0x/ace-0.5b-gguf:BF16
```

Lemonade

How to use ansh0x/ace-0.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ansh0x/ace-0.5b-gguf:BF16

Run and chat with the model

lemonade run user.ace-0.5b-gguf-BF16

List all available models

lemonade list

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

More info: GitHub Repository ```

Downloads last month: 41

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Model tree for ansh0x/ace-0.5b-gguf

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(213)

this model