Model details

Before vs After (ROUGE scores)

See eval_results.json for full before/after comparison on the test set.

Usage

python
Copy code
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "amarshiv86/p07-sre-lora-phi3")
tokenizer = AutoTokenizer.from_pretrained("amarshiv86/p07-sre-lora-phi3")

prompt = "<|user|>\nWhat steps should I take when a pod is in CrashLoopBackOff?<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training data

SRE runbook Q&A pairs covering: incident response, Kubernetes troubleshooting, SLO/SLI definitions, on-call procedures, and post-mortem templates.

Links

• GitHub Repo
• HF Space Demo
• Training Dataset