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cs-net-v4
gary2oos/cs-net-v4
cs-net-v4 is a reinforcement learning model from gary2oos. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Trained checkpoints for CS-NET v4 — a Transformer-based framework for Counter-Strike 2 game-state forecasting. Full open-source code (training, inference, 3D visualizer) is available at github.com/Gary2005/cs-net.
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Public
Updated Sep 7, 2026
Repo size
1.4 GB
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.pt1.4 GB · 100%
At a glance
- Task
- Reinforcement Learning
- License
- mit
- Access
- Public
- Created
- Sep 7, 2026
- Updated
- Sep 7, 2026
- SHA
- 32d9bae4
Model card
From the Hugging Face model README
CS-NET v4 — CS2 Path Prediction & Spatial-Only Models
Trained checkpoints for CS-NET v4 — a Transformer-based framework for Counter-Strike 2 game-state forecasting. Full open-source code (training, inference, 3D visualizer) is available at github.com/Gary2005/cs-net.
All checkpoints use the Pro architecture (d_model=768, ~138.7M params),
see config/pretrain-a100-pro.yaml in the source repo.
Files
| File | Task | Description |
|---|---|---|
cs-net-v4-pro.pt | Path prediction (pre-training) | 600k steps. Full model: {model, global_step}. Auto-regressively predicts each player's future movement path (16 ticks = 4 s) from a window of the full game state (10 players + bomb + projectiles + raycast depth maps). |
pretrain-v4-pro-win_rate.pt | Spatial-only winrate | Per-player team win probability from a single tick of the game state. {task, model_state, head_state, config} — embedder + spatial transformer + linear head. |
pretrain-v4-pro-alive_end.pt | Spatial-only alive_end | Per-player probability of being alive at round end. Same format. |
pretrain-v4-pro-future_kill.pt | Spatial-only future_kill | Per-player probability of getting a kill after the current tick. Same format. |
Quick start
git clone https://github.com/Gary2005/cs-net.git
cd cs-net
conda create -n cs2demo python=3.10 && conda activate cs2demo
pip install -r requirements.txt
pip install huggingface_hub
# download all 4 checkpoints
python scripts/download_checkpoints.py --repo-id gary2oos/cs-net-v4
# verify they load correctly (architecture match, finite weights, forward pass)
python scripts/test_checkpoints.py --models-dir checkpoints
Inference
# Path prediction
from scripts.prediction_engine import PredictionEngine
engine = PredictionEngine("config/pretrain-a100-pro.yaml",
"checkpoints/cs-net-v4-pro.pt",
device="cpu", maps_dir="maps/optimized_obj_files")
result = engine.predict_at_tick(sample, query_tick=120) # sample = round-level dict
# Spatial-only (winrate / alive_end / future_kill)
from scripts.spatial_only_predictor import SpatialOnlyPredictor
predictor = SpatialOnlyPredictor("checkpoints", device="cpu")
out = predictor.predict_round_full(sample) # per-tick per-player probabilities
3D visualizer
python visualizer/server.py --port 5000 \
--checkpoint checkpoints/cs-net-v4-pro.pt \
--spatial-model-dir checkpoints \
--device cpu
# open http://127.0.0.1:5000/
License
MIT (see source repo).
- Task
- Reinforcement Learning
- License
- mit
- Languages
- en
- Created
- Sep 7, 2026
- Updated
- Sep 7, 2026