Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shashankN777/evacos2-7b-orchestrator-artifacts to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shashankN777/evacos2-7b-orchestrator-artifacts to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="shashankN777/evacos2-7b-orchestrator-artifacts",
max_seq_length=2048,
)EvacOS2 Public Training Artifacts
This repository hosts the public LoRA adapter artifacts, metrics, logs, and generated configs selected for the EvacOS2 OpenEnv India Hackathon 2026 submission.
Canonical environment Space: https://huggingface.co/spaces/shashankN777/evacos2-openenv
Canonical code repo: https://github.com/sai-shashankN/EvacOS2
Evidence Status
The visible submitted checkpoints in this repo are 3B floor-specialist H200 canaries for fire, flood, and gas response lanes. They are not claimed as final 7B orchestrator quality checkpoints. The tracked fixed-suite scorecard in the code repo is baseline-only; the tracked learning evidence is the fire/flood/gas specialist canary and training-signal artifact trail.
Public Checkpoint Paths
| Specialist | Public checkpoint path | Run type |
|---|---|---|
| Fire floor specialist | floor-specialists/fire/h200-canary3-10/checkpoints/latest |
10-step H200 canary |
| Flood floor specialist | floor-specialists/flood/h200-canary3-10/checkpoints/latest |
10-step H200 canary |
| Gas floor specialist | floor-specialists/gas/h200-canary-10/checkpoints/latest |
10-step H200 canary |
Each run folder includes:
checkpoints/latest/lora_adapter/floor_agent/adapter_model.safetensorscheckpoints/latest/lora_adapter/floor_agent/adapter_config.json- generated training config YAML
- training metrics CSV
- JSONL traces and summaries
- train/contrast exit-code markers
Download
hf download shashankN777/evacos2-7b-orchestrator-artifacts \
--include "floor-specialists/fire/h200-canary3-10/**" \
--include "floor-specialists/flood/h200-canary3-10/**" \
--include "floor-specialists/gas/h200-canary-10/**" \
--local-dir outputs/hf_public_artifacts
Evaluate A Restored Canary Checkpoint
Run this from the EvacOS2 code repo after downloading the artifacts:
CHECKPOINT_DIR=outputs/hf_public_artifacts/floor-specialists/fire/h200-canary3-10/checkpoints/latest
CONFIG_PATH=outputs/hf_public_artifacts/floor-specialists/fire/h200-canary3-10/generated.remote-unsloth-3b-fire-floor-specialist-h200-canary3-10.yaml
METRICS_PATH=outputs/hf_public_artifacts/floor-specialists/fire/h200-canary3-10/remote-unsloth-3b-fire-floor-specialist-h200-canary3-10-metrics.csv
python -m evaluation.demo_bundle \
--trained-checkpoint "$CHECKPOINT_DIR" \
--config "$CONFIG_PATH" \
--training-metrics-path "$METRICS_PATH" \
--output-dir outputs/demo_bundle_fire_h200_canary
For the checked-in baseline-only fixed suite, see demo/results/submission_scorecard_baseline.md in the code repo. For the tracked learning-signal summary, see demo/results/specialist_canary50_report.md and demo/results/3b_specialist_canary50_scores.csv.
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for shashankN777/evacos2-7b-orchestrator-artifacts to start chatting