MachineLearningML: >-
Continued Pretraining Language Models on Millions of Synthetic Tabular
Prediction Tasks Scales In-Context ML
license: apache-2.0
base_model:
- Qwen/Qwen2.5-7B-Instruct
MachineLearningLM
model summary
Can LLMs learn from 1,000 in-context examples?
Introducing MachineLearningLM 🧪📊 — a model continuously pretrained on millions of synthetic tabular ML tasks, enabling robust many-shot in-context learning.
📈 Scales from 8 to 1,024 examples
📈 ~15% improvement on unseen tabular tasks compared to o3-mini / GPT-5-mini / Qwen-2.5-7B
🌲 Random-Forest–level robustness
🧠 MMLU score: 75.4%
📄 Read the paper: https://arxiv.org/abs/2509.06806
GitHub: https://github.com/HaoAreYuDong/MachineLearningLM
evaluation and validation
We have developed an automated evaluation framework — simply configure the parameters to easily perform validation and evaluation. The code is now open-sourced at our GitHub.
Quick Start
pip install -r requirements.txt
python ./src/evaluation/model_pred/dl_model_pred.py \
--input_dir ./demo_input.jsonl \
--output_dir ./demo_output.jsonl \
--model_name MachineLearningLM/MachineLearningLM-7B-v1
Automated pipeline
# modify the evaluate_parameters.sh file
source evaluate_parameters.sh
./scripts/evaluate_pipeline.sh
For more usage details, please visit our GitHub.