Azure Cloud Solution Architect - Qwen 3.5 0.8B (SFT LoRA)

A Supervised Fine-Tuned (SFT) LoRA adapter that turns Qwen 3.5 0.8B into an Azure Cloud Solution Architect.

What This Model Does

  • Answers questions about Azure architecture patterns and best practices
  • Identifies Azure services in architecture diagrams
  • Explains cloud design decisions (networking, compute, storage, security, etc.)
  • Trained on 1,678 Q&A pairs scraped from Azure Architecture Center

Training Details

Parameter Value
Base Model unsloth/Qwen3.5-0.8B
Method Supervised Fine-Tuning (SFT) with LoRA
LoRA Rank 16
Dataset thegovind/azure-architecture-vqa (1,678 train / 187 test)
Training Time 42.6 minutes on RTX 4090
Final Loss 0.6517
Steps 210 (1 epoch)
Hardware 1x NVIDIA RTX 4090 (24GB)

How to Use

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

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3.5-0.8B", 
    torch_dtype=torch.float16, 
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "thegovind/azure-architect-qwen35-0.8b")
tokenizer = AutoTokenizer.from_pretrained("thegovind/azure-architect-qwen35-0.8b")

prompt = "What Azure service is best for global content delivery?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Pipeline

This is the first stage of a two-stage fine-tuning pipeline:

Base Model (Qwen 3.5 0.8B)
    → SFT (this model) — learns Azure knowledge
        → GRPO (thegovind/azure-architect-qwen35-0.8b-grpo) — learns structured reasoning

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