azure-advisor-qwen25-0.5b

Azure Advisor Qwen2.5-0.5B (SFT)

Fine-tuned Qwen/Qwen2.5-0.5B-Instruct to generate Azure Advisor-style recommendations using Supervised Fine-Tuning (SFT).

Model Description

This model has been trained to analyze Azure workload configurations and generate structured recommendations across 5 Azure Advisor categories:

• Cost - Cost optimization recommendations
• Security - Security posture improvements
• Performance - Performance optimization suggestions
• OperationalExcellence - Operational best practices
• HighAvailability - Reliability and availability improvements

Training Details

Training Metrics

Loss Trajectory

1.76 -> 1.06 -> 0.47 -> 0.24 -> 0.13 -> 0.064 -> 0.050 -> 0.044 -> 0.038 -> 0.034 -> 0.029

Evaluation (5 Reward Functions, max 10.0)

Output Format

The model generates structured output with:

1. <ANALYSIS> - Reasoning about the workload state
2. <RECOMMENDATIONS> - JSON array of recommendation objects
3. <SUMMARY> - Brief summary of key recommendations

Each recommendation includes: category, impact, resourceId, problem, solution, potentialBenefits, evidence, nextSteps, confidence.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "thegovind/azure-advisor-qwen25-0.5b")
tokenizer = AutoTokenizer.from_pretrained("thegovind/azure-advisor-qwen25-0.5b")

messages = [
    {"role": "system", "content": "You are an Azure Advisor assistant..."},
    {"role": "user", "content": "Analyze this Azure workload and provide recommendations..."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

W&B Training Dashboard

• SFT Run: wandb.ai/thegovind/azure-advisor-model/runs/quzg7fgs
• Project: wandb.ai/thegovind/azure-advisor-model

Related Resources

• GRPO Model: thegovind/azure-advisor-qwen25-0.5b-grpo - Further refined with reward-based GRPO training
• SFT Dataset: thegovind/azure-advisor-sft - 410 training examples across 15 scenario types
• GRPO Benchmark: thegovind/azure-advisor-grpo-benchmark - 106 evaluation examples with ground truth