---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- mlx-community/NVIDIA-Nemotron-Nano-9B-v2-4bits
library_name: mlx
tags:
- mlx
- lora
- sql
- financialSQL
- finance
- nemotron
- dpo
---

# finsql-mlx-nemotron-nano-9b-v2-4bits

  This is a LoRA adapter for financial SQL generation, fine-tuned on mlx-community/NVIDIA-Nemotron-Nano-9B-v2-4bits.

  ## Latest Finetuning
![Screenshot 2025-10-11 at 13.28.30](https://cdn-uploads.huggingface.co/production/uploads/68c5d7248a0237bcabdce5cc/l5jMjmOvP40366Dwj1Hqr.png)

  #### HF Model Path: [gccmorgoth/finsql-mlx-nemotron-nano-9b-v2-4bits](https://huggingface.co/gccmorgoth/finsql-mlx-nemotron-nano-9b-v2-4bits)
  
  ## Finetuning Details
  - **Method**: Direct Preference Optimization (DPO)
  - **Checkpoint**: Iteration 600
  - **Validation Loss**: 0.069
  - **Training Loss**: 0.0002
  - **Learning Rate**: Cosine decay with warmup
  - **LoRA Rank**: 16
  - **Beta**: 0.1

  ## Performance
  - Validation loss: 0.069 (optimal convergence point)
  - Selected at iteration 600 to prevent overfitting
  - DPO training for improved preference alignment on financial SQL tasks

  ## Model Selection
  - **Checkpoint**: Iteration 600 selected based on validation loss curve
  - **Rationale**: Optimal balance between training convergence and
  generalization
  - **Training Dynamics**: Early stopping before overfitting (val loss
  increased at iter 700+)

  ## Dataset
  This model was fine-tuned on financial text-to-sql data pairs, specifically
  the [FinSQLBull dataset](https://bull-text-to-sql-benchmark.github.io), to improve SQL query generation for financial
  databases and tables.

  ## Usage

  Recommended prompt format to specify:
  # Database: [database_name]
  [Schema information]

  ## Task
  [Natural language question about the data]

  Constraint: [Any specific constraints]

  SQL: [Model Generated SQL Query]

  ## Sample Prompt Format

  Database: company_financials

  Table: revenue (id, company, year, revenue, profit)

  Task

  What was the total revenue for all companies in 2023?

  SQL: [Model Generated SQL Query]

## Python

  ```python
  from mlx_lm import load, generate

  model, tokenizer = load("your-username/your-model-name")
  response = generate(model, tokenizer, prompt="Your prompt here")