---
language:
- en
license: other
base_model:
- mistralai/Devstral-Small-2505
tags:
- text-to-sql
- sql
- mistral
- transformers
- safetensors
pipeline_tag: text-generation
library_name: transformers
---

# Devstral SQLCoder SFT

This model is a full-parameter SFT checkpoint for SQL generation, trained from `mistralai/Devstral-Small-2505` and exported to Hugging Face safetensors format.

## Model Details

- Base model: `mistralai/Devstral-Small-2505`
- Architecture: `MistralForCausalLM`
- Precision used in training: bf16
- Max sequence length (training config): 4096
- Export format: sharded `safetensors` with `model.safetensors.index.json`

## Training Data (Merged)

The SFT run merged the following datasets:

- spider
- bird
- bird23-train-filtered
- synsql-2.5m
- wikisql
- gretelai-synthetic
- sql-create-context

## Intended Use

- Text-to-SQL research and experimentation
- SQL generation benchmarks and evaluation pipelines

## Limitations

- This model may generate incorrect SQL and should be validated before production use.
- Performance depends on prompt format, schema context quality, and decoding settings.
- Evaluate safety and compliance requirements before deployment.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_or_path = "<hf-username-or-org>/<model-repo>"

tokenizer = AutoTokenizer.from_pretrained(repo_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_or_path,
    torch_dtype="bfloat16",
)
```

## Local Files Included

- `config.json`
- `generation_config.json`
- `tekken.json`
- `model-00001-of-00021.safetensors` ... `model-00021-of-00021.safetensors`
- `model.safetensors.index.json`

## Citation

If you use this model, please cite this repository:

- https://github.com/ai-twinkle/twinkle-sqlcoder