Update README.md

README.md

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 ---
-base_model: seeklhy/OmniSQL-32B
-library_name: peft
+license: apache-2.0
+language:
+- en
+base_model:
+- seeklhy/OmniSQL-32B
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.15.2
+# 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 **BIRD** 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)