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"
Judge v4: fast kills counted; detector reference to match
Browse files- .gitattributes +1 -0
- README.md +7 -5
- detector/scorer.json +4 -4
- judge/judge-v4.Q4_K_M.gguf +3 -0
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README.md
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| `detector/scorer.onnx` | a 1D CNN that scores each kill's aim trajectory (136 KB) |
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| `detector/scorer.json` | its architecture and the frozen clean-player reference a score is read against |
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## What they are for
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per-player ROC-AUC 0.93 in match-grouped cross-validation. Line of sight comes
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from ray casts against each map's collision mesh, not the game's spotting flag,
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which is biased against snipers.
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evidence shown in the text (never on the ban label), with clean players' 95th
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matches its targets
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## Credits and licences
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| `detector/scorer.onnx` | a 1D CNN that scores each kill's aim trajectory (136 KB) |
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| `detector/scorer.json` | its architecture and the frozen clean-player reference a score is read against |
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| `judge/judge-v4.Q4_K_M.gguf` | Qwen3.5-4B fine-tuned (QLoRA) to write a verdict from the evidence, Q4_K_M |
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## What they are for
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per-player ROC-AUC 0.93 in match-grouped cross-validation. Line of sight comes
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from ray casts against each map's collision mesh, not the game's spotting flag,
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which is biased against snipers.
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- **Judge (v4):** fine-tuned on generated verdicts whose targets depend only on
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evidence shown in the text (never on the ban label), with clean players' 95th
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and 99th percentiles printed beside every measurement. On 206 held-out cases it
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matches its targets 92% of the time, convicts 29% of banned players, accuses
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2.8% of clean-labelled ones (each where the evidence itself is past the clean
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99th percentile), and cites no number absent from the evidence. On 341 pro
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players from 35 HLTV matches it accused none.
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## Credits and licences
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detector/scorer.json
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"corrections_mean": 1.4285714285714286,
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"wall_aim_share": 0.12626262626262627,
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"visible_share": 0.8205128205128205,
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"angle_at_first_visible_median": 3.9900760650634766,
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"corrections_mean": 1.4285714285714286,
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"wall_aim_share": 0.12626262626262627,
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"visible_share": 0.8205128205128205,
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"fast_kills": 0.0,
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"angle_at_first_visible_median": 3.9900760650634766,
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"snap_max": 63.369140625,
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"zero_motion_share": 0.31338862559241704,
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0.38680926916221037,
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"angle_at_first_visible_median": [
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size 2783446976
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