Update README.md

README.md

@@ -146,6 +146,32 @@ The model underwent full fine-tuning (not parameter-efficient methods like LoRA)
 
 The training code can be found [here](Code/Fine-tuning.py).
 
+GRPO + SFT on 7.3k data from [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis/viewer) with:
+
+```py
+NUM_EPOCHS = 2
+PER_DEVICE_BATCH = 4
+GRADIENT_ACCUMULATION = 4
+LEARNING_RATE = 2e-5
+WEIGHT_DECAY = 0.01
+WARMUP_STEPS = 100
+```
+
+(--> Breadth)
+
+Second SFT on ~3k examples with:
+
+```py
+NUM_EPOCHS = 3
+PER_DEVICE_BATCH = 4
+GRADIENT_ACCUMULATION = 4
+LEARNING_RATE = 2e-5
+WEIGHT_DECAY = 0.01
+WARMUP_STEPS = 50
+```
+
+(--> Depth)
+
 ## Model Capabilities
 
 The model employs a systematic, ontology-inspired analysis framework that decomposes queries into structured XML representations. This analytical approach (which identifies domains, intents, concepts, relations, and ambiguities) aims to enhance the quality of the rephrased query output. For every query, it provides domain classification and intent detection with confidence scores that sum to `1.0`, properly handling both unambiguous cases (single candidate with confidence `1.0`) and ambiguous cases (multiple candidates with distributed confidence). The model can optionally extract explicit and implicit concepts, identify relations between entities using subject-predicate-object triples, and normalize ambiguous terms when disambiguation would improve retrieval.
@@ -312,37 +338,6 @@ print(analysis)
 
 To assess the model's ability to produce correctly formatted outputs, we evaluated structural conformance across 1,000 examples from the test split of [krogoldAI/rag-ambiguous-queries](https://huggingface.co/datasets/krogoldAI/rag-ambiguous-queries), comparing QueryRefiner-0.5B-v0.1 against its base model [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). The evaluation measured adherence to the required XML schema, including tag presence, well-formedness, element ordering, and confidence score formatting.
 
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-GRPO + SFT on 7.3k data from [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis/viewer) with:
-
-```py
-NUM_EPOCHS = 2
-PER_DEVICE_BATCH = 4
-GRADIENT_ACCUMULATION = 4
-LEARNING_RATE = 2e-5
-WEIGHT_DECAY = 0.01
-WARMUP_STEPS = 100
-```
-
-(--> Breadth)
-
-Second SFT on ~3k examples with:
-
-```py
-NUM_EPOCHS = 3
-PER_DEVICE_BATCH = 4
-GRADIENT_ACCUMULATION = 4
-LEARNING_RATE = 2e-5
-WEIGHT_DECAY = 0.01
-WARMUP_STEPS = 50
-```
-
-(--> Depth)
-
 #### Table 1 - Structural validity metrics (% of outputs meeting each requirement)
 
 |Metric|Qwen2.5-0.5B-Instruct|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO|