MachineLearningLM/MachineLearningLM-7B-v1

MachineLearningLM

This repository contains the model presented in the paper MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining.

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-Instruct

🌲 ​​Random-Forest–level numerical modeling 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 repository](https://github.com/HaoAreYuDong/MachineLearningLM).

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

For more usage details, please visit our GitHub.

Quants of Checkpoints

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

Tabicl Evaluation

This part of the code needs to run in an environment with the tabicl and openpyxl libraries installed.

The evaluation code for tabicl is placed separately in the ./src/evaluation/tabicl_evaluate.py file. Use ./scripts/tabicl_evaluate.sh to obtain the evaluation results for tabicl.

Use --datasets to specify the datasets to be evaluated, and --sample_sizes to indicate the number of shots.

If multiple datasets need to be evaluated, separate them with spaces. To evaluate all CSV files in the input folder, use all.

Prior_data

MachineLearningLM uses the code from tabicl to generate prior data.

Use ./scripts/generate_data.sh to generate the prior data. It generates the corresponding .pt and .csv files, and normalizes the feature values in the CSV files to the range of 0–999, as we did in the paper.

Parameter Introduction（refer to the comments in the file tabicl\src\tabicl\prior\dataset.py）

Data Scale & Structure

Batch Configuration

Sequence Length Control

Train-Test Split

Generation Method

Computation Settings

Train

MachineLearningLM uses the LLaMA-Factory framework for training.

Training Environment Configuration

cd ./third_party/LLaMA-Factory
pip install -e ".[torch,metrics]" --no-build-isolation
pip install wandb

Use ./scripts/train.sh for training.

Project Structure

MachineLearningLM/
├──src/
|   ├──evaluation/
│   │   ├── data_prep/          # Data preprocessing and chunking utilities
│   │   ├── prompt_gen/         # Prompt generation for deep learning models
│   │   ├── model_pred/         # Model inference (ML and DL prediction engines)
│   │   ├── result_proc/        # 5-layer evaluation architecture and metrics processing
│   │   ├── zero_summary/       # Result summarization and report generation
│   │   └── tabicl_evaluate.py
│   └──prior_data
│       └── pt_to_csv.py     
├── scripts/
│   ├── single_process/         # Sequential execution shell scripts
│   ├── multi_process/          # Parallel execution shell scripts (with _mp suffix)
│   ├── evaluate_parameters.sh  # Global parameter configuration
|   ├── evaluate_pipeline.sh    # automated pipeline
|   ├── generate_data.sh
|   ├── tabicl_evaluate.sh
|   └── train.sh
├── datahub_inputs/
│   ├── data_demo/          # Demo datasets for testing
│   └── data_raw/           # Raw input datasets
├── third_party/
│   ├── tabicl/          
│   └── LLaMA-Factory/   
├── requirements.txt        # Python dependencies for Evaluation Framework
├── README.md
├── README_zh.md
├── THIRD_PARTY_NOTICES.md
└── LICENSE