mistral-nemo-text-to-sql/README.md

language, base_model, tags, metrics, model_creator, pipeline_tag, datasets, library_name

language	base_model	tags	metrics	model_creator	pipeline_tag	datasets	library_name
en	mistralai/Mistral-Nemo-Instruct-2407	text-to-sql mistral-nemo spider peft qlora	execution_accuracy exact_match	NBAmine	text-generation	gretelai/synthetic_text_to_sql xlangai/spider NBAmine/xlangai-spider-with-context	transformers

Mistral-Nemo-12B-Text-to-SQL

Model Overview

This is the full-precision (BF16), merged version of a Mistral-Nemo-12B model Parameter-Efficient Fine-Tuned for high-performance Text-to-SQL generation. This model is the result of merging LoRA adapters—trained via a two-phase curriculum learning strategy—back into the base weights.

It is designed to serve as the "Source of Truth" for further optimizations (like AWQ or GGUF) and represents the peak predictive performance of the training pipeline before any quantization-related drift.

• Base Model: mistralai/Mistral-Nemo-Base-2407
• Primary Task: Natural Language to SQL generation with DDL context.
• Output Format: Standalone SQL queries compatible with standard SQL engines.

Training Methodology

The model was developed using an MLOps pipeline on dual T4 GPUs in Kaggle.

1. Curriculum Learning Strategy

The model underwent a two-stage training process:

• Phase 1 (Syntactic Alignment): Focused on SQL syntax, basic keywords, and simple schema mapping.
• Phase 2 (Logical Alignment): Introduced complex reasoning tasks including multiple JOIN operations, nested subqueries, and set operations (UNION, INTERSECT).

2. Fine-Tuning Details

• Technique: QLoRA (Rank 16, Alpha 32)
• Quantization (during training): 4-bit NF4
• Optimizer: Paged AdamW 8-bit
• Hardware: 2x NVIDIA T4 (Kaggle).

Evaluation Results

Evaluated on the Spider validation set:

• Execution Accuracy (EX): 69.5%
• Exact Match (EM): 61.2%
• Max Context Length: 2048 tokens

Architecture Specs

The merged weights utilize the standard Mistral-Nemo 12B architecture:

• Parameters: 12.2B
• Layers: 40
• Attention: Grouped Query Attention (GQA) with 8 KV heads.
• Vocabulary Size: 128k (Tekken Tokenizer)
• VRAM Requirements: ~24GB for inference in BF16/FP16.

Template used during training

prompt = "Context: {DDL}
Question: {NL_QUERY}
Answer:"