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

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
```

Improve model card: Add pipeline tag, library name, and expand usage details

by nielsr HF Staff - opened Sep 13, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+124

-6

nielsr

Sep 13, 2025

This PR aims to improve the model card for MachineLearningLM by:

Adding pipeline_tag: text-generation to the metadata. This ensures the model is discoverable on the Hugging Face Hub for relevant tasks (e.g., https://huggingface.co/models?pipeline_tag=text-generation), reflecting its capability for in-context learning and general text generation.
Adding library_name: transformers to the metadata. The config.json and tokenizer_config.json files confirm compatibility with the transformers library, which will enable the automated "How to use" widget on the model page.
Removing an improperly formatted and redundant descriptive entry from the metadata.
Expanding the model card content to include detailed sections on "Tabicl Evaluation", "Prior_data", "Train", and "Project Structure" directly from the official GitHub README. This provides a more comprehensive overview of the model's functionality and evaluation framework.

These changes enhance the model's discoverability and provide users with a richer, more complete understanding of its capabilities and usage on the Hugging Face Hub.

Improve model card: Add pipeline tag, library name, and expand usage details445adf8d

MachineLearningLM changed pull request status to merged Sep 13, 2025

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