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.

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/mradermacher/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

Parameter	Type	Description
min_features	int	Minimum number of features per dataset
max_features	int	Maximum number of features per dataset
max_classes	int	Maximum number of target classes
min_seq_len	int	Minimum samples per dataset. Uses max_seq_len if None
max_seq_len	int	Maximum samples per dataset （Not Include）

Batch Configuration

Parameter	Type	Description
batch_size	int	Total number of datasets to generate per batch
batch_size_per_gp	int	Number of datasets per group (shared characteristics)
batch_size_per_subgp	int	Number of datasets per subgroup (similar causal structures). Defaults to batch_size_per_gp if None

Sequence Length Control

Parameter	Type	Description
log_seq_len	bool	Sample sequence length from log-uniform distribution if True
seq_len_per_gp	bool	Sample sequence length per group (enables variable-sized datasets)
replay_small	bool	Occasionally sample smaller sequences for model robustness

Train-Test Split

Parameter	Type	Description
min_train_size	int/float	Start position/ratio for train split (int: absolute, float: fractional)
max_train_size	int/float	End position/ratio for train split (int: absolute, float: fractional)

Generation Method

Parameter	Type	Description
prior_type	str	Prior type: 'mlp_scm', 'tree_scm', or 'mix_scm' (random selection)
fixed_hp	dict	Fixed structural configuration parameters
sampled_hp	dict	Parameters sampled during generation

Computation Settings

Parameter	Type	Description
n_jobs	int	Number of parallel jobs (-1 = use all processors)
num_threads_per_generate	int	Number of threads per generation job
device	str	Computation device ('cpu' or 'cuda')

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