metadata

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://huggingface.co/papers/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

pipeline

# modify the evaluate_parameters.sh file
source evaluate_parameters.sh

# Option 1  End-to-End Pipeline
./scripts/evaluate_pipeline.sh

# Option 2  Parallel Processing
./scripts/multi_process/data_prep.sh
./scripts/multi_process/prompt_gen.sh  # For deep learning only
./scripts/multi_process/model_pred.sh
./scripts/multi_process/evaluation.sh
./scripts/multi_process/report.sh

# Option3   Sequential Processing
./scripts/single_process/data_prep.sh
./scripts/single_process/prompt_gen.sh  # For deep learning only
./scripts/single_process/model_pred.sh
./scripts/single_process/evaluation.sh
./scripts/single_process/report.sh

Quants

https://huggingface.co/mradermacher/MachineLearningLM-7B-v1-GGUF

For more usage details, please visit our GitHub.