Text Generation
PEFT
Safetensors
llama
grpo
reinforcement-learning
devops
incident-response
openenv
unsloth
conversational
How to use from
Unsloth StudioInstall 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 Arijit-07/aria-devops-llama8b to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Arijit-07/aria-devops-llama8b to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Arijit-07/aria-devops-llama8b",
max_seq_length=2048,
)Quick Links
ARIA — DevOps Incident Response Agent
Llama-3.1-8B fine-tuned with GRPO
Trained on the ARIA DevOps Incident Response live RL environment using GRPO.
Training Results
| Task | Baseline | Fine-tuned | Improvement |
|---|---|---|---|
| easy | 0.320 | 0.685 | +0.365 |
| medium | 0.050 | 0.378 | +0.328 |
| hard | 0.190 | 0.869 | +0.679 |
| bonus | 0.152 | 0.682 | +0.530 |
Setup
- Algorithm: GRPO
- Base: Llama-3.1-8B-Instruct
- LoRA rank: 32, alpha: 64
- Episodes: 160 (40 per task)
- GPU: NVIDIA L4, 162 minutes
- Framework: Unsloth + HuggingFace TRL
Links
- Downloads last month
- -
Model tree for Arijit-07/aria-devops-llama8b
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct Finetuned
unsloth/Meta-Llama-3.1-8B-Instruct
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 Arijit-07/aria-devops-llama8b to start chatting