Instructions to use csoai/csoai-gate-ft2 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 csoai/csoai-gate-ft2 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 csoai/csoai-gate-ft2:F16 # Run inference directly in the terminal: llama cli -hf csoai/csoai-gate-ft2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf csoai/csoai-gate-ft2:F16 # Run inference directly in the terminal: llama cli -hf csoai/csoai-gate-ft2:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf csoai/csoai-gate-ft2:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf csoai/csoai-gate-ft2:F16
Use Docker
docker model run hf.co/csoai/csoai-gate-ft2:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use csoai/csoai-gate-ft2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "csoai/csoai-gate-ft2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "csoai/csoai-gate-ft2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/csoai/csoai-gate-ft2:F16
- Ollama
How to use csoai/csoai-gate-ft2 with Ollama:
ollama run hf.co/csoai/csoai-gate-ft2:F16
- Unsloth Desktop
- Pi
How to use csoai/csoai-gate-ft2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/csoai-gate-ft2: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": "csoai/csoai-gate-ft2:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use csoai/csoai-gate-ft2 with Docker Model Runner:
docker model run hf.co/csoai/csoai-gate-ft2:F16
- Lemonade
How to use csoai/csoai-gate-ft2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull csoai/csoai-gate-ft2:F16
Run and chat with the model
lemonade run user.csoai-gate-ft2-F16
List all available models
lemonade list
- Hermes Agent
How to use csoai/csoai-gate-ft2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/csoai-gate-ft2: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 csoai/csoai-gate-ft2:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use csoai/csoai-gate-ft2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/csoai-gate-ft2: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 "csoai/csoai-gate-ft2: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"
densify: live GET gspc 22·22·0 + Hub 885/885/0 + gspc-verify (printer only)
Browse files
README.md
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@@ -30,3 +30,13 @@ Wrapped by `csoai/oowm-router`. Living-training-attestation, not a certification
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---
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**Related live board:** GSPC **22 axis · 22 measured** (14 model-comparison + 8 deterministic-fact) — `GET https://councilof.ai/api/gspc`. This is a CSOAI council-specialist model: it is in the measured fleet and transparently labelled, but our own models are **not** published as public per-axis leaders on the neutral board (own leaders excluded on 8 of 14 model axes). Measurement, not certification.
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---
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**Related live board:** GSPC **22 axis · 22 measured** (14 model-comparison + 8 deterministic-fact) — `GET https://councilof.ai/api/gspc`. This is a CSOAI council-specialist model: it is in the measured fleet and transparently labelled, but our own models are **not** published as public per-axis leaders on the neutral board (own leaders excluded on 8 of 14 model axes). Measurement, not certification.
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<!-- csoai-hf-gspc-densify-2026-09-07 -->
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## Living GET (printer — densify 2026-09-07)
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- Live board: `GET https://councilof.ai/api/gspc` → **22 axis · 22 measured** (**22·22·0**). Quote `totals.public_count`.
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- Hub cells (third-party Hub — **not** the board; **not** cards 336 / root 168 / index 335): `GET https://councilof.ai/api/hub-cards` → **885/885/0**.
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- Free verify: https://councilof.ai/gspc-verify
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- This README is a **printer** of those GETs. Do not freeze a score table. Never certify / never stamp MEASURED / never sell rank. Fetch fail → **UNCHECKABLE**.
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<!-- /csoai-hf-gspc-densify-2026-09-07 -->
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