Instructions to use MagicNoThief/cs2-overwatch 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 MagicNoThief/cs2-overwatch 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 MagicNoThief/cs2-overwatch:Q4_K_M # Run inference directly in the terminal: llama cli -hf MagicNoThief/cs2-overwatch:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MagicNoThief/cs2-overwatch:Q4_K_M # Run inference directly in the terminal: llama cli -hf MagicNoThief/cs2-overwatch:Q4_K_M
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 MagicNoThief/cs2-overwatch:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MagicNoThief/cs2-overwatch:Q4_K_M
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 MagicNoThief/cs2-overwatch:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MagicNoThief/cs2-overwatch:Q4_K_M
Use Docker
docker model run hf.co/MagicNoThief/cs2-overwatch:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use MagicNoThief/cs2-overwatch with Ollama:
ollama run hf.co/MagicNoThief/cs2-overwatch:Q4_K_M
- Unsloth Desktop
- Pi
How to use MagicNoThief/cs2-overwatch with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MagicNoThief/cs2-overwatch:Q4_K_M
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": "MagicNoThief/cs2-overwatch:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use MagicNoThief/cs2-overwatch with Docker Model Runner:
docker model run hf.co/MagicNoThief/cs2-overwatch:Q4_K_M
- Lemonade
How to use MagicNoThief/cs2-overwatch with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MagicNoThief/cs2-overwatch:Q4_K_M
Run and chat with the model
lemonade run user.cs2-overwatch-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use MagicNoThief/cs2-overwatch with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MagicNoThief/cs2-overwatch:Q4_K_M
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 MagicNoThief/cs2-overwatch:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MagicNoThief/cs2-overwatch with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MagicNoThief/cs2-overwatch:Q4_K_M
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 "MagicNoThief/cs2-overwatch:Q4_K_M" \ --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"
CS2 Overwatch review: models
The two models behind Overwatch review, an offline reviewer for Counter-Strike 2 demos that runs on your own PC. The app downloads these files itself, pinned to one commit and checked against their SHA-256; you do not need to fetch them by hand.
| file | what it is |
|---|---|
detector/scorer.onnx |
a 1D CNN that scores each kill's aim trajectory (136 KB) |
detector/scorer.json |
its architecture and the frozen clean-player reference a score is read against |
judge/judge-v3.Q4_K_M.gguf |
Qwen3.5-4B fine-tuned (QLoRA) to write a verdict from the evidence, Q4_K_M |
What they are for
To help a person review a demo: which players and which kills deserve a look, and why, in terms that can be checked in the demo. Not to ban anyone automatically. A high score says a player's kills look unlike clean players' kills in the training data; it is evidence to examine, not proof.
How they were made
- Detector: trained on CS2CD (795 matches, 317 with a VAC-banned player); per-player ROC-AUC 0.93 in match-grouped cross-validation. Line of sight comes from ray casts against each map's collision mesh, not the game's spotting flag, which is biased against snipers.
- Judge (v3): fine-tuned on generated verdicts whose targets depend only on evidence shown in the text (never on the ban label), with clean players' 95th and 99th percentiles printed beside every measurement. On held-out cases it matches its targets 87% of the time, accuses 1.9% of clean players, and cited no number absent from the evidence in 206 answers.
Credits and licences
- These models are released under CC BY-NC 4.0: free to use, share and adapt for non-commercial purposes, with credit. The app's code is AGPL-3.0-or-later.
- The judge is a fine-tune of Qwen3.5-4B
by the Qwen team, licensed under Apache-2.0; its licence is included as
judge/LICENSE-Qwen3.5-Apache-2.0.txt. - Both models were trained on the CS2CD dataset (CC-BY-4.0) by Mille Mei Zhen Loo and Gert Lužkov.
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