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"
Download a32-efficiency.json from cbarkinozer/Reflex-1: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/cbarkinozer/Reflex-1/resolve/main/a32-efficiency.json
- Command line
-
hf download hf://cbarkinozer/Reflex-1/a32-efficiency.json
-
curl -L -o a32-efficiency.json https://huggingface.co/cbarkinozer/Reflex-1/resolve/main/a32-efficiency.json
1.03 kB
| { | |
| "points": [ | |
| { | |
| "id": "run-01", | |
| "label": "Reflex-1", | |
| "accuracy": 91.11111111111111, | |
| "latency_s": 31.364144939161097, | |
| "sampled_pss_mib": 1090.6455078125 | |
| }, | |
| { | |
| "id": "qwen-legacy-cached-fresh180", | |
| "label": "Base Qwen, cached app router", | |
| "accuracy": 54.44444444444444, | |
| "latency_s": 35.395459384916684, | |
| "sampled_pss_mib": 854.8271484375 | |
| } | |
| ], | |
| "observed_frontiers": { | |
| "latency_s": [ | |
| "run-01" | |
| ], | |
| "sampled_pss_mib": [ | |
| "run-01", | |
| "qwen-legacy-cached-fresh180" | |
| ] | |
| }, | |
| "input_sha256": "9928dd52c5b4946850e58502e014eefef54ff63aa2047f99283b167e3351a37a", | |
| "generator": "tools/plot_a32_efficiency.py", | |
| "matplotlib_version": "3.9.4", | |
| "definition": "Maximize accuracy and minimize the named cost; strict improvement in at least one objective.", | |
| "limitations": "Single runs; profile/policy and starting temperature differ. Sampled PSS is not in-flight peak memory. No measured energy or cross-model Decision Index frontier." | |
| } | |