Instructions to use MachineLearningLM/MachineLearningLM-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MachineLearningLM/MachineLearningLM-7B-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MachineLearningLM/MachineLearningLM-7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MachineLearningLM/MachineLearningLM-7B-v1")
model = AutoModelForMultimodalLM.from_pretrained("MachineLearningLM/MachineLearningLM-7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MachineLearningLM/MachineLearningLM-7B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MachineLearningLM/MachineLearningLM-7B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MachineLearningLM/MachineLearningLM-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MachineLearningLM/MachineLearningLM-7B-v1

SGLang

How to use MachineLearningLM/MachineLearningLM-7B-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MachineLearningLM/MachineLearningLM-7B-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MachineLearningLM/MachineLearningLM-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MachineLearningLM/MachineLearningLM-7B-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MachineLearningLM/MachineLearningLM-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MachineLearningLM/MachineLearningLM-7B-v1 with Docker Model Runner:
```
docker model run hf.co/MachineLearningLM/MachineLearningLM-7B-v1
```

MachineLearningLM-7B-v1 / README.md

HaoyuDong

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	---
	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

	```bash
	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
	```bash
	# 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.