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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MachineLearningLM/MachineLearningLM-7B-v1")
model = AutoModelForCausalLM.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

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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-Instruct

🌲 **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
```

For more usage details, please visit our GitHub.