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 benchmark-summary.json from cbarkinozer/Reflex-1: direct link, hf CLI and curl.
- Browser
- Download file 36.4 kB
-
https://huggingface.co/cbarkinozer/Reflex-1/resolve/main/benchmark-summary.json
- Command line
-
hf download hf://cbarkinozer/Reflex-1/benchmark-summary.json
-
curl -L -o benchmark-summary.json https://huggingface.co/cbarkinozer/Reflex-1/resolve/main/benchmark-summary.json
36.4 kB
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| "n": 180, | |
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| "accuracy_difference": 0.6777777777777778, | |
| "paired_bootstrap_95ci": [ | |
| 0.6, | |
| 0.75 | |
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| "exact_mcnemar_two_sided_p": 2.0549580818052442e-33, | |
| "bootstrap_seed": 3407, | |
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| "qwen-legacy-fresh180": { | |
| "n": 180, | |
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| }, | |
| "qwen-legacy-cached-fresh180": { | |
| "n": 180, | |
| "reflex_only_correct": 70, | |
| "baseline_only_correct": 4, | |
| "accuracy_difference": 0.36666666666666664, | |
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| "exact_mcnemar_two_sided_p": 1.2898469490022716e-16, | |
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| } | |
| }, | |
| "complete": true, | |
| "original_expanded_queue_complete": false, | |
| "required_jobs": [ | |
| "qwen-fresh180", | |
| "qwen-legacy-cached-fresh180", | |
| "qwen-legacy-fresh180", | |
| "run-01" | |
| ], | |
| "deferred_jobs": [ | |
| "beta-fresh180", | |
| "reflex-historical90", | |
| "qwen-historical90", | |
| "qwen-cached-fresh180", | |
| "reflex-typed_decisions", | |
| "reflex-banking77", | |
| "reflex-clinc150", | |
| "beta-typed_decisions", | |
| "beta-banking77", | |
| "beta-clinc150" | |
| ], | |
| "privacy": "Aggregate metrics only; no held-out requests, per-case responses, reviewer IDs or device-property dumps.", | |
| "runtime": "Android CPU build11443 f0c41e0168dfd4b5ef72b21d1a311b24cc7a894a; 4 inference/batch threads; Q4_K_M; phone ctx2048, general ctx4096; USB powered", | |
| "cohort_caveat": "Fresh180 and original historical90 are distinct; historical PyTorch reference used reviewed88. Raw Qwen prompt/JSON grammar treatment is specifically documented, not all possible Qwen inference configurations." | |
| } | |