Trained on the ClusterMind Chaos Arena environment via SFT warm-start + online RL (grpo). Base weights are frozen; only the LoRA adapter is updated (r=8, target_modules=["q_proj","v_proj"]).

Training stack

Model load + LoRA: transformers (Unsloth FastLanguageModel when available, else transformers + bitsandbytes 4-bit + peft)
SFT phase: trl.SFTTrainer
RL phase: in-tree GRPO/PPO/REINFORCE loop (TRL's GRPOTrainer OOMs on T4 because it holds all K trajectories' computation graphs simultaneously; ours is two-phase: no-grad rollout collection then per-step backward)
Hub push: huggingface_hub.push_to_hub + upload_file

Training summary

field	value
base model	`Qwen/Qwen2.5-0.5B-Instruct`
engine	`transformers`
SFT trainer	`trl.SFTTrainer`
RL algo	`grpo` (auto: trl present -> using episode-level GRPO)
trainable params	540,672 / 11.973056694274142 (4515739.08%)
SFT episodes	16
RL episodes	24
eval episodes	8
eval mean reward	10.46
frozen base	True
lora only	True
quick mode	True

How to load

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen2.5-0.5B-Instruct"
adapter = "Kabs-123/clustermind-lora"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Files in this repo

adapter_model.safetensors — LoRA weights
adapter_config.json — LoRA config (r, alpha, target modules)
tokenizer.json etc. — tokenizer of the base model
training_logs.jsonl — per-step reward + loss + metrics
trained_results.json — full training summary

Evaluation

The trained agent is benchmarked against five heuristic baselines on 8 chaos scenarios at curriculum levels 3–5. See trained_results.json for the full eval breakdown.