Safetensors
GGUF
English
intent-classification
decision-model
tool-routing
qwen3.5
unsloth
lora
conversational
Instructions to use cbarkinozer/Reflex-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cbarkinozer/Reflex-1 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 cbarkinozer/Reflex-1:Q8_0 # Run inference directly in the terminal: llama cli -hf cbarkinozer/Reflex-1:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cbarkinozer/Reflex-1:Q8_0 # Run inference directly in the terminal: llama cli -hf cbarkinozer/Reflex-1:Q8_0
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 cbarkinozer/Reflex-1:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf cbarkinozer/Reflex-1:Q8_0
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 cbarkinozer/Reflex-1:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf cbarkinozer/Reflex-1:Q8_0
Use Docker
docker model run hf.co/cbarkinozer/Reflex-1:Q8_0
- LM Studio
- Jan
- Ollama
How to use cbarkinozer/Reflex-1 with Ollama:
ollama run hf.co/cbarkinozer/Reflex-1:Q8_0
- Unsloth Desktop
- Pi
How to use cbarkinozer/Reflex-1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cbarkinozer/Reflex-1:Q8_0
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": "cbarkinozer/Reflex-1:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use cbarkinozer/Reflex-1 with Docker Model Runner:
docker model run hf.co/cbarkinozer/Reflex-1:Q8_0
- Lemonade
How to use cbarkinozer/Reflex-1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cbarkinozer/Reflex-1:Q8_0
Run and chat with the model
lemonade run user.Reflex-1-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use cbarkinozer/Reflex-1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cbarkinozer/Reflex-1:Q8_0
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 cbarkinozer/Reflex-1:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use cbarkinozer/Reflex-1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cbarkinozer/Reflex-1:Q8_0
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 "cbarkinozer/Reflex-1:Q8_0" \ --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"
Commit History
2 days ago
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Update README.md
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Apply author-selected Apache-2.0 license and preserve source attribution
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Finalize release documentation and correct artifact validation status
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Document separately released MOBILE_ROUTING9 test cohort
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Document separately released MOBILE_ROUTING9 test cohort
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Link verified MOBILE_ROUTING9 training dataset
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Point phone training data to GitHub
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Link exact reviewed phone training dataset
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Fix resource table Markdown column count
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Use verified 780M parameter label for Reflex-1
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Record exact deployed text parameter count
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Clarify matched evaluation methods, intended use and compact-model labels
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Focus on base, Reflex-1 and matched Laya results across four benchmarks
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Document measured A32 routing, decision-model benchmarks and efficiency tradeoffs
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3 days ago
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Add Reflex-1 selected decision adapter and Q4_K_M GGUF research release
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initial commit
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