thegovind
/

azure-advisor-qwen25-0.5b-grpo

@@ -1,199 +1,123 @@
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 ---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - azure
+  - advisor
+  - azure-advisor
+  - grpo
+  - reinforcement-learning
+  - lora
+  - qwen2.5
+  - fine-tuned
+datasets:
+  - thegovind/azure-advisor-sft
+  - thegovind/azure-advisor-grpo-benchmark
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# Azure Advisor Qwen2.5-0.5B (GRPO)
+Fine-tuned [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) with SFT followed by Group Relative Policy Optimization (GRPO) to generate Azure Advisor-style recommendations.
+## Model Description
+This model builds on the [SFT model](https://huggingface.co/thegovind/azure-advisor-qwen25-0.5b) with additional reward-based training using GRPO (rejection sampling + iterative SFT). It generates 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 Pipeline
+### Phase 1: SFT (Supervised Fine-Tuning)
+- 200 training steps on 348 examples
+- Loss: 1.76 -> 0.029
+- Baseline reward: 0.80/10 -> 3.72/10
+### Phase 2: GRPO (Group Relative Policy Optimization)
+- 3 iterations of rejection sampling + iterative SFT
+- 25 training prompts per iteration, 4 samples per prompt
+- Top-2 high-reward samples kept per prompt
+- 30 SFT steps per iteration
+| Iteration | Training Loss | Generation Avg Reward |
+|-----------|--------------|----------------------|
+| 1 | 0.069 | 4.19 |
+| 2 | 0.048 | 4.30 |
+| 3 | 0.040 | 4.44 |
+### Hill Climbing Verification
+```
+Pre-SFT baseline:  0.80/10
+Post-SFT:          3.43/10  (+2.63)
+GRPO iter losses:  0.069 -> 0.048 -> 0.040 (decreasing)
+GRPO gen rewards:  4.19 -> 4.30 -> 4.44 (increasing)
+HILL CLIMBING CONFIRMED
+```
+## Training Configuration
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
+| Method | SFT + GRPO (rejection sampling) |
+| LoRA Rank / Alpha | 16 / 32 |
+| Quantization | 4-bit QLoRA (NF4) |
+| GRPO Iterations | 3 |
+| Samples per Prompt | 4 |
+| Top-K Selection | 2 |
+| Steps per Iteration | 30 |
+| Learning Rate | 5e-5 |
+| Hardware | NVIDIA RTX 3090 (24GB) |
+## 5 Reward Functions (max 10.0 total)
+| Function | Weight | Description |
+|----------|--------|-------------|
+| Format Compliance | 1.5 | Correct XML tags (`<ANALYSIS>`, `<RECOMMENDATIONS>`, `<SUMMARY>`) and valid JSON |
+| Category Correctness | 2.0 | Valid Advisor categories (Cost, Security, Performance, etc.) |
+| Grounding Quality | 2.0 | Claims supported by input evidence, no hallucinated resource IDs |
+| Actionability | 2.0 | Concrete, feasible next steps with specific Azure actions |
+| Completeness | 2.5 | Coverage of all issues with proper recommendation schema fields |
+## Usage
+```python
+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-grpo")
+tokenizer = AutoTokenizer.from_pretrained("thegovind/azure-advisor-qwen25-0.5b-grpo")
+messages = [
+    {"role": "system", "content": "You are an Azure Advisor assistant. Analyze the workload and provide recommendations."},
+    {"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](https://wandb.ai/thegovind/azure-advisor-model/runs/quzg7fgs)
+- **GRPO Run**: [wandb.ai/thegovind/azure-advisor-model/runs/v6h8i0hr](https://wandb.ai/thegovind/azure-advisor-model/runs/v6h8i0hr)
+- **Project**: [wandb.ai/thegovind/azure-advisor-model](https://wandb.ai/thegovind/azure-advisor-model)
+## Related Resources
+- **SFT Model**: [thegovind/azure-advisor-qwen25-0.5b](https://huggingface.co/thegovind/azure-advisor-qwen25-0.5b) - Base SFT model before GRPO
+- **SFT Dataset**: [thegovind/azure-advisor-sft](https://huggingface.co/datasets/thegovind/azure-advisor-sft) - 410 training examples
+- **GRPO Benchmark**: [thegovind/azure-advisor-grpo-benchmark](https://huggingface.co/datasets/thegovind/azure-advisor-grpo-benchmark) - 106 evaluation examples