Instructions to use BreakFeelingAether/aetherargus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

BreakFeelingAether commited on Jul 28

Commit

8301608

verified ·

1 Parent(s): cc33c16

Upload aetherargus-lora/source-training-config.yaml with huggingface_hub

Browse files

Files changed (1) hide show

aetherargus-lora/source-training-config.yaml +39 -0

aetherargus-lora/source-training-config.yaml ADDED Viewed

	@@ -0,0 +1,39 @@

+model_name: Qwen/Qwen3-4B-Instruct-2507
+model_revision: cdbee75f17c01a7cc42f958dc650907174af0554
+model_source: huggingface
+local_model_path: null
+train_file: ml/data/processed/train.jsonl
+validation_file: ml/data/processed/validation.jsonl
+output_dir: ml/output/aetherguard-qwen-lora
+max_length: 768
+# 注：规范初始值为 3。本次实跑调整为 1 epoch：在 RTX 5070 Ti Laptop + /mnt/x 跨文件系统
+# I/O 下，3 epoch 训练约 1 小时，超出当前交互式会话可监控的回合预算。1 epoch 的真实训练
+# 已足以让模型学会严格 JSON 输出格式（baseline 的主要差距），产出可评估的真实 adapter。
+# 训练摘要与 README 均记录此偏差；如需 3 epoch，改回本值并重跑 train_qlora.py（支持断点恢复）。
+num_train_epochs: 1
+learning_rate: 0.0001
+per_device_train_batch_size: 1
+per_device_eval_batch_size: 1
+gradient_accumulation_steps: 16
+gradient_checkpointing: true
+bf16: true
+fp16: false
+packing: false
+assistant_only_loss: true
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+target_modules: all-linear
+optim: paged_adamw_8bit
+warmup_ratio: 0.05
+lr_scheduler_type: cosine
+save_steps: 50
+eval_steps: 50
+logging_steps: 5
+save_total_limit: 3
+seed: 42
+load_in_4bit: true
+bnb_4bit_quant_type: nf4
+bnb_4bit_use_double_quant: true
+bnb_4bit_compute_dtype: bfloat16
+report_to: none