---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
tags:
- reinforcement-learning
- lora
- peft
- trl
- unsloth
- grpo
- clustermind
license: apache-2.0
---

# ClusterMind Chaos Arena — LoRA adapter (GRPO)

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.