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dnaihao/phi-3-mini-tablellama
phi-3-mini-tablellama is a text generation model from dnaihao. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Replication of TableLlama, trained from Phi-3-mini-4k-instruct on the corresponding instruction-tuning corpus.
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From the Hugging Face model README
Replication of TableLlama, trained from Phi-3-mini-4k-instruct on the corresponding instruction-tuning corpus.
Released alongside the EACL 2026 Findings paper "What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects" (Deng et al., 2026) as an additional artefact extending the paper's experiments — the main 3 base × 4 training-data grid in the paper covers Mistral-v0.3, OLMo, and Phi-3-small at the 7B scale; this model adds another base-model variant trained on the same corpus.
| Base model | microsoft/Phi-3-mini-4k-instruct |
| Training corpus | tablellama_train.json from dnaihao/Table-Instructs |
| Method | Full SFT via LLaMA-Factory |
| Learning rate | 5e-7 |
Full hyperparameter sweep, ablations, and per-benchmark numbers are reported in the paper.
Per-{model, benchmark} eval scripts and parsed metrics are available at github.com/dnaihao/table-sft-eacl-2026/tree/main/eval/phi-3-mini-tablellama. Raw model outputs (generated_predictions.jsonl) are released as the dataset dnaihao/table-sft-eval-predictions.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dnaihao/phi-3-mini-tablellama")
model = AutoModelForCausalLM.from_pretrained(
"dnaihao/phi-3-mini-tablellama",
torch_dtype="auto",
device_map="auto",
)
This model inherits the license of its base model (microsoft/Phi-3-mini-4k-instruct: mit).
@inproceedings{deng-etal-2026-really,
title = "What Really Matters for Table {LLM}s? A Meta-Evaluation of Model and Data Effects",
author = "Deng, Naihao and Zhang, Sheng and Zhu, Henghui and Chang, Shuaichen and Zhang, Jiani and Li, Alexander Hanbo and Hang, Chung-Wei and Kobayashi, Hideo and Hu, Yiqun and Ng, Patrick",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2026",
year = "2026",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.195/",
doi = "10.18653/v1/2026.findings-eacl.195"
}