Instructions to use AtomGradient/Qwen3.5-9B-Chronos-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AtomGradient/Qwen3.5-9B-Chronos-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AtomGradient/Qwen3.5-9B-Chronos-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use AtomGradient/Qwen3.5-9B-Chronos-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AtomGradient/Qwen3.5-9B-Chronos-4bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AtomGradient/Qwen3.5-9B-Chronos-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use AtomGradient/Qwen3.5-9B-Chronos-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AtomGradient/Qwen3.5-9B-Chronos-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AtomGradient/Qwen3.5-9B-Chronos-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AtomGradient/Qwen3.5-9B-Chronos-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use AtomGradient/Qwen3.5-9B-Chronos-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AtomGradient/Qwen3.5-9B-Chronos-4bit"
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 AtomGradient/Qwen3.5-9B-Chronos-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AtomGradient/Qwen3.5-9B-Chronos-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AtomGradient/Qwen3.5-9B-Chronos-4bit"
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 "AtomGradient/Qwen3.5-9B-Chronos-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
AtomGradient/Qwen3.5-9B-Chronos-4bit
ไธญๆ็ฎไป
Qwen3.5-9B-Chronos-4bit ๆฏไธไธบ Neural Imprint Chronos ๆ้ ็็ซฏไพงๅ็ๆ็ปญๅญฆไน ๏ผNative Continual Learning๏ผไธ็จๅบๅบงๆจกๅ๏ผ้ๆญ่ฝฝ Chronos ไธ็จๅ ๆ ธ๏ผChronos Kernel๏ผ ่ฟ่กใ
ๆ ธๅฟ็นๆง
- ็ซฏไพงๅ็ๆ็ปญๅญฆไน ๏ผๆ ้ไบ็ซฏ่ฎญ็ปๆๅๅไผ ๆญๅพฎ่ฐ๏ผๅจๆฌๅฐ่ฎพๅค่ฟ่ก่ฟ็จไธญๅณๅฏๅฎๆถๅธๆถใๆดๆฐไธๅทฉๅบ็จๆท็้ฟๆๅๅฅฝใ็ๆดปไน ๆฏใ่ทจไบไปถๅ ๆๅ ณ่ๅไธๅฑๅทฅๅ ท่ฐ็จๅ่ฎฎใ
- ๆๅฎไฝ็งฏ่ฎฐๅฟไธ่ทจ็ซฏๆต่ฝฌ๏ผๆ็ปญๅญฆไน ไบง็็็จๆท่ฎฐๅฟไปฅๆๅฎ่ถ ็ดงๅ็ถๆๅ ๅฝขๅผๆไน ๅ๏ผไธ้ไบคไบ่ฝฎๆฌก็บฟๆง่จ่๏ผๆฏๆๅจๆๆบใๆก้ขๅทฅไฝ็ซใๆบ่ฝๅบง่ฑไธๅ ท่บซ็ป็ซฏไน้ด็ง็บง่ฟ็งปไธๆขๅคใ
- ๆฐๆฎไธๅบ็ซฏไธ็จๆท่ตไบงไธปๆ๏ผๆๆๅญฆไน ใๅญๅจไธๆจ็ๅ จ็จๅจ็จๆทๆฌๅฐ่ฎพๅคๅฎๆ๏ผ็ถๆ่ตไบงๅๆบไธปไธๅ ๆ ธๅ้ไธปๆไฟๆค๏ผ็กฎไฟไธชไบบ่ฎฐๅฟๆฐๆฎ็็ปๅฏน็งๅฏๆงใ
ไฝฟ็จ่ฏดๆ๏ผๆฌๆจกๅไธ Neural Imprint Chronos ไธ็จๅ ๆ ธ ๆทฑๅบฆๅๅ่ฆๅ๏ผไธๆฏๆ่ฑ็ฆป Chronos ๅ ๆ ธ็ดๆฅไฝฟ็จ้็จๆจ็ๆกๆถ่ฟ่กใ
English Overview
Qwen3.5-9B-Chronos-4bit is a dedicated on-device backbone model purpose-built for Neural Imprint Chronos native continual learning. It requires the Chronos Kernel runtime to operate.
Key Capabilities
- On-Device Native Continual Learning: Continuously learns, updates, and consolidates user preferences, temporal habits, multi-event causal context, and custom tool-calling protocols in real time on edge devicesโwithout cloud retraining or gradient backpropagation.
- Constant-Envelope Memory & Cross-Device Mobility: Continual learning states are persisted in an ultra-compact, constant-size state bundle with zero byte growth over time, enabling instant cold-start restoration and seamless migration across smartphones, desktops, vehicle cockpits, and embodied robotics.
- Strict On-Device Privacy & User Sovereignty: User data physically never leaves the device. Learned memory states are cryptographically bound to the device owner and the Chronos runtime.
Runtime Requirement: This model is tightly coupled with the Neural Imprint Chronos Kernel and is not intended for standalone execution on generic inference runtimes without the Chronos kernel.
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