Text Generation
Transformers
Safetensors
GGUF
PEFT
Chinese
English
qlora
cybersecurity
security
intrusion-detection
structured-output
json
ollama
aetherguard
aetherargus
conversational
Instructions to use BreakFeelingAether/aetherargus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BreakFeelingAether/aetherargus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BreakFeelingAether/aetherargus")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BreakFeelingAether/aetherargus", device_map="auto") - PEFT
How to use BreakFeelingAether/aetherargus with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BreakFeelingAether/aetherargus with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf BreakFeelingAether/aetherargus:F16 # Run inference directly in the terminal: llama cli -hf BreakFeelingAether/aetherargus:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf BreakFeelingAether/aetherargus:F16 # Run inference directly in the terminal: llama cli -hf BreakFeelingAether/aetherargus:F16
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 BreakFeelingAether/aetherargus:F16 # Run inference directly in the terminal: ./llama-cli -hf BreakFeelingAether/aetherargus:F16
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 BreakFeelingAether/aetherargus:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf BreakFeelingAether/aetherargus:F16
Use Docker
docker model run hf.co/BreakFeelingAether/aetherargus:F16
- LM Studio
- Jan
- vLLM
How to use BreakFeelingAether/aetherargus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BreakFeelingAether/aetherargus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BreakFeelingAether/aetherargus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BreakFeelingAether/aetherargus:F16
- SGLang
How to use BreakFeelingAether/aetherargus 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 "BreakFeelingAether/aetherargus" \ --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": "BreakFeelingAether/aetherargus", "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 "BreakFeelingAether/aetherargus" \ --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": "BreakFeelingAether/aetherargus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use BreakFeelingAether/aetherargus with Ollama:
ollama run hf.co/BreakFeelingAether/aetherargus:F16
- Unsloth Desktop
- Pi
How to use BreakFeelingAether/aetherargus with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BreakFeelingAether/aetherargus:F16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "BreakFeelingAether/aetherargus:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use BreakFeelingAether/aetherargus with Docker Model Runner:
docker model run hf.co/BreakFeelingAether/aetherargus:F16
- Lemonade
How to use BreakFeelingAether/aetherargus with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BreakFeelingAether/aetherargus:F16
Run and chat with the model
lemonade run user.aetherargus-F16
List all available models
lemonade list
- Hermes Agent
How to use BreakFeelingAether/aetherargus with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BreakFeelingAether/aetherargus:F16
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 BreakFeelingAether/aetherargus:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use BreakFeelingAether/aetherargus with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BreakFeelingAether/aetherargus:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "BreakFeelingAether/aetherargus:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download aetherargus-lora/training-summary.json from BreakFeelingAether/aetherargus: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/BreakFeelingAether/aetherargus/resolve/main/aetherargus-lora/training-summary.json
- Command line
-
hf download hf://BreakFeelingAether/aetherargus/aetherargus-lora/training-summary.json
-
curl -L -o training-summary.json https://huggingface.co/BreakFeelingAether/aetherargus/resolve/main/aetherargus-lora/training-summary.json
1.31 kB
| { | |
| "completedAt": "2026-07-28T00:41:07.773648+00:00", | |
| "smokeTest": false, | |
| "modelReference": "/mnt/x/repos/Networksafe/ml/output/base-model", | |
| "outputDirectory": "/mnt/x/repos/Networksafe/ml/output/aetherguard-qwen-lora", | |
| "elapsedSeconds": 740.8194129880001, | |
| "peakGpuAllocatedGiB": 5.381433486938477, | |
| "peakGpuReservedGiB": 5.615234375, | |
| "startup": { | |
| "totalParameters": 2222325248, | |
| "trainableParameters": 16515072, | |
| "trainablePercent": 0.7431437866650201, | |
| "trainSamples": 958, | |
| "validationSamples": 122, | |
| "effectiveBatchSize": 16, | |
| "gpuName": "NVIDIA GeForce RTX 5070 Ti Laptop GPU", | |
| "gpuTotalMemoryGiB": 11.94000244140625, | |
| "maxLength": 768, | |
| "loraR": 8, | |
| "loraAlpha": 16 | |
| }, | |
| "trainMetrics": { | |
| "train_runtime": 651.199, | |
| "train_samples_per_second": 1.471, | |
| "train_steps_per_second": 0.092, | |
| "total_flos": 8434343709060096.0, | |
| "train_loss": 0.6697107156117758, | |
| "epoch": 1.0 | |
| }, | |
| "evaluationMetrics": { | |
| "eval_loss": 0.5009067058563232, | |
| "eval_runtime": 23.6746, | |
| "eval_samples_per_second": 5.153, | |
| "eval_steps_per_second": 5.153, | |
| "epoch": 1.0 | |
| }, | |
| "globalStep": 60, | |
| "epoch": 1.0, | |
| "adapterFiles": { | |
| "adapterModel": true, | |
| "adapterConfig": true, | |
| "tokenizerConfig": true, | |
| "trainerState": true | |
| } | |
| } | |