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twinkle-ai/twinkle-sqlcoder

sourceHugging Faceotherupdated 7mo agoView on Hugging Face
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Model Card

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