| --- |
| license: mit |
| base_model: Qwen/Qwen3.5-4B |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - tabular |
| - agent |
| - tool-use |
| - tabular-foundation-model |
| --- |
| |
| # TabPilot: Empowering Small Language Models for Natural-Language Tabular Prediction |
|
|
| Code: [github.com/fearlessAAA/TabPilot](https://github.com/fearlessAAA/TabPilot) |
|
|
| TabPilot is an agentic framework that couples a 4B small language model with a **plug-and-play |
| frozen tabular foundation model (TFM)** through **declarative predictive query compilation**. An |
| **Execution Agent** iteratively compiles each natural-language request into a structured |
| prediction query and validates it through leakage-safe tools; the frozen TFM executes the query |
| by in-context inference; a **Response Agent** synthesizes the TFM predictions into a grounded |
| answer. The agents are trained for unseen tables with a three-stage local-to-global curriculum: |
| **Privileged Semantic On-Policy Self-Distillation (PS-OPSD)**, **Structure-Aware GRPO (SA-GRPO)** |
| with an outcome-label-free, TFM-agnostic structural reward, and **Teacher-Guided Tabular Response |
| Distillation (TTRD)**. |
|
|
| This repository holds the trained agents (backbone: Qwen3.5-4B, non-thinking mode): |
|
|
| | Folder | Model | |
| |---|---| |
| | `execution-agent/fold<k>/` | Execution Agent: full weights after PS-OPSD → SA-GRPO | |
| | `response-agent/fold<k>/` | Response Agent: TTRD LoRA adapter (rank 32) for `Qwen/Qwen3.5-4B` | |
|
|
| There is one model pair per fold `k` of the five-fold cross-validation over TopBench source tables |
| (`data/splits.json` in the code repository). Fold `k` was trained only on that fold's training |
| tables, so evaluate it only on fold `k`'s test tables. |
|
|
| ## Usage |
|
|
| ```bash |
| hf download XiaoboX/TabPilot --include "execution-agent/fold0/*" "response-agent/fold0/*" --local-dir checkpoints |
| ``` |
|
|
| Serve both agents with vLLM and run TabPilot as described in the code repository's README. The |
| TFM is plug-and-play: the agents were trained without calling it, and the code repository uses |
| TabPFN v3.5 by default. |
|
|
| ## License |
|
|
| MIT. The models are fine-tuned from [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) |
| (Apache 2.0) on data derived from [TopBench](https://github.com/LAMDA-Tabular/TopBench) (MIT). |
|
|
