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GradeSQL-32B-ORM-Spider

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 ---
-base_model: seeklhy/OmniSQL-32B
-library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Framework versions
-- PEFT 0.15.2

 ---
+license: apache-2.0
+language:
+- en
+base_model:
+- seeklhy/OmniSQL-32B
 ---
+# GradeSQL-32B — Outcome Reward Model for Text-to-SQL
+## Model Description
+**GradeSQL-32B** is an Outcome Reward Model (ORM) designed to evaluate the semantic correctness of SQL queries generated from natural language questions in Text-to-SQL tasks. Rather than relying on syntactic heuristics or majority votes, GradeSQL-32B assigns a confidence score indicating whether a candidate SQL query faithfully answers the user's question based on the database schema.
+Built on top of the **OmniSQL-32B** base model and finetuned on the **SPIDER** dataset, GradeSQL-32B provides a robust semantic scoring mechanism to improve query selection and alignment with user intent.
+## Intended Use
+- **Reranking Candidate SQL Queries:** Use GradeSQL-32B to assign semantic correctness scores and select the best SQL query among multiple candidates generated by LLMs.
+- **Enhancing Text-to-SQL Pipelines:** Integrate as a reward or reranking model to improve execution accuracy and semantic fidelity in Text-to-SQL systems.
+- **Evaluation and Research:** Analyze the semantic alignment of SQL queries with natural language questions to identify and mitigate errors.
+## Finetuning Configuration
+The following **LoRA configuration** was used to train this model:
+- **R**: `16` (rank of the low-rank matrices)
+- **Lora Alpha**: `64` (scaling factor for the low-rank update)
+- **Target Modules**: `{q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj}`
+- **Lora Dropout**: `0.05`
+- **Bias**: `"none"` (bias terms are frozen)
+- **FP16**: `True` (half-precision training)
+- **Learning Rate**: `7e-5`
+- **Train Batch Size**: `5`
+- **Num. Train Epochs**: `50`
+## Usage Example
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+prompt = """Question: What is the total horses record for each farm, sorted ascending?
+CREATE TABLE competition_record (
+    Competition_ID number, -- example: [1, 2]
+    Farm_ID number, -- example: [2, 3]
+    Rank number, -- example: [1, 2]
+    PRIMARY KEY (Competition_ID),
+    CONSTRAINT fk_competition_record_competition_id FOREIGN KEY (Competition_ID) REFERENCES farm_competition (Competition_ID),
+    CONSTRAINT fk_competition_record_farm_id FOREIGN KEY (Farm_ID) REFERENCES farm (Farm_ID)
+);
+CREATE TABLE city (
+    City_ID number, -- example: [1, 2]
+    Status text, -- example: ['Town', 'Village']
+    PRIMARY KEY (City_ID)
+);
+CREATE TABLE farm_competition (
+    Competition_ID number, -- example: [1, 2]
+    Host_city_ID number, -- example: [1, 2]
+    PRIMARY KEY (Competition_ID),
+    CONSTRAINT fk_farm_competition_host_city_id FOREIGN KEY (Host_city_ID) REFERENCES city (City_ID)
+);
+CREATE TABLE farm (
+    Farm_ID number, -- example: [1, 2]
+    Total_Horses number, -- example: [5056.5, 5486.9]
+    Total_Cattle number, -- example: [8374.5, 8604.8]
+    PRIMARY KEY (Farm_ID)
+);
+What is the total horses record for each farm, sorted ascending?
+SQL: SELECT SUM(Total_Horses) AS Total_Horses, Farm_ID
+FROM farm
+GROUP BY Farm_ID
+ORDER BY SUM(Total_Horses) ASC;
+Is the SQL correct?"""
+base_model = AutoModelForCausalLM.from_pretrained("seeklhy/OmniSQL-32B", torch_dtype="auto", device_map="auto")
+peft_model = PeftModel.from_pretrained(base_model, "sisinflab-ai/GradeSQL-32B-ORM-Bird")
+orm_model = peft_model.merge_and_unload()
+orm_tokenizer = AutoTokenizer.from_pretrained("seeklhy/OmniSQL-32B", use_fast=True)
+del base_model
+del peft_model
+inputs = orm_tokenizer(prompt, return_tensors="pt").to(orm_model.device)
+with torch.no_grad():
+    outputs = orm_model.generate(**inputs, max_new_tokens=1, return_dict_in_generate=True, output_scores=True, use_cache=False)
+    generated_ids = outputs.sequences[0, len(inputs.input_ids[0]):]
+    yes_token_id = orm_tokenizer.convert_tokens_to_ids("ĠYes")
+    no_token_id = orm_tokenizer.convert_tokens_to_ids("ĠNo")
+    yes_no_pos = None
+    for i, token_id in enumerate(generated_ids):
+        if token_id in [yes_token_id, no_token_id]:
+            yes_no_pos = i
+            break
+    if yes_no_pos is None:
+        print("[Warning]: No 'Yes' or 'No' token found in the generated output.")
+        print("[Score]: 0.5")
+    logits = outputs.scores[yes_no_pos]
+    probs = torch.softmax(logits, dim=-1)
+    yes_prob = probs[0, yes_token_id].item()
+    generated_answer = "Yes" if generated_ids[yes_no_pos] == yes_token_id else "No"
+    if generated_answer == "Yes":
+        print("[Score]: ", yes_prob)
+    elif generated_answer == "No":
+        print("[Score]: ", 0)