初始化项目，由ModelHub XC社区提供模型

Model: NBAmine/mistral-nemo-text-to-sql
Source: Original Platform

README.md

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+---
+language:
+- en
+base_model: mistralai/Mistral-Nemo-Instruct-2407
+tags:
+- text-to-sql
+- mistral-nemo
+- spider
+- peft
+- qlora
+metrics:
+- execution_accuracy
+- exact_match
+model_creator: NBAmine
+pipeline_tag: text-generation
+datasets:
+- gretelai/synthetic_text_to_sql
+- xlangai/spider
+- NBAmine/xlangai-spider-with-context
+library_name: transformers
+---
+
+# Mistral-Nemo-12B-Text-to-SQL
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repo-181717?logo=github&logoColor=white)](https://github.com/NBAmine/Nemo-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}<br>Question: {NL_QUERY}<br>Answer:"