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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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How to Get Started with the Model
Use the code below to get started with the model.
1!pip install -q transformers accelerate bitsandbytes
2
3import torch
4from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
5import json
6import re
7
8MODEL_NAME = "nareshhere/sre-monitor"
9
10tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
11
12model = AutoModelForCausalLM.from_pretrained(
13 MODEL_NAME,
14 device_map={"": "cuda"},
15 load_in_4bit=True,
16 llm_int8_enable_fp32_cpu_offload=True,
17 torch_dtype=torch.float16
18)
19
20pipe = pipeline(
21 "text-generation",
22 model=model,
23 tokenizer=tokenizer,
24 max_new_tokens=150,
25 do_sample=False
26)
27
28def generate_sre_json(user_query: str) -> dict:
29 prompt = f"""
30You are an SRE assistant.
31Respond ONLY in valid JSON with keys: action, metric, entity_hint, duration.
32Use double quotes for keys and strings. Do not add explanations.
33
34User: {user_query}
35Response:
36"""
37 output_text = pipe(prompt)[0]["generated_text"]
38 raw = output_text.split("Response:")[-1].strip()
39 raw = raw.replace("'", '"')
40 raw = re.sub(r'[\n\r]', '', raw)
41 raw = re.sub(r'\)$', '', raw)
42 try:
43 return json.loads(raw)
44 except json.JSONDecodeError:
45 return {"error": "Invalid JSON", "raw_output": raw}
46
47user_input = "Check CPU usage on api server for last 10 minutes"
48result = generate_sre_json(user_input)
49print(result)
-----------------------------
Test
-----------------------------
user_input = "Check CPU usage on api server for last 10 minutes"
result = generate_sre_json(user_input)
print(result)
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Training Details
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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