Instructions to use InternScience/MLExpert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InternScience/MLExpert with Transformers:

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

pipe = pipeline("text-generation", model="InternScience/MLExpert")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("InternScience/MLExpert")
model = AutoModelForMultimodalLM.from_pretrained("InternScience/MLExpert", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InternScience/MLExpert with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/InternScience/MLExpert

SGLang

How to use InternScience/MLExpert 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 "InternScience/MLExpert" \
    --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": "InternScience/MLExpert",
		"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 "InternScience/MLExpert" \
        --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": "InternScience/MLExpert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use InternScience/MLExpert with Docker Model Runner:
```
docker model run hf.co/InternScience/MLExpert
```

YoungDubby commited on 3 days ago

Commit

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1 Parent(s): 15498f2

add github link

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README.md +10 -11

README.md CHANGED Viewed

@@ -10,10 +10,9 @@ tags:
   - tool-use
   - reinforcement-learning
 ---
 # M1: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
-[🤗 Hugging Face](https://huggingface.co/InternScience/M1)
 **M1** is a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. It organizes experiments, writes and revises code, and improves solutions through sustained interaction with execution environments. Hierarchical agentic SFT develops general tool-use and domain-specific optimization skills, while **Windowed Interaction Reinforcement Learning (WIRL)** trains multi-turn decisions using feedback from completed experiments.
@@ -125,13 +124,13 @@ vllm serve InternScience/M1 \
 ### Recommended Sampling Parameters
-| Parameter | Value |
-| :--- | ---: |
-| `temperature` | 1.0 |
-| `top_p` | 0.95 |
-| `top_k` | 20 |
-| `min_p` | 0.0 |
-| `presence_penalty` | 1.5 |
-| `repetition_penalty` | 1.0 |
-These parameters apply to both thinking and non-thinking modes. Set `chat_template_kwargs.enable_thinking` to `true` to enable thinking or `false` to disable it. Machine-readable profiles are provided in [inference_profiles.json](./inference_profiles.json).

   - tool-use
   - reinforcement-learning
 ---
 # M1: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
+[💻 GitHub](https://github.com/InternScience/M1) · [🤗 Hugging Face](https://huggingface.co/InternScience/M1)
 **M1** is a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. It organizes experiments, writes and revises code, and improves solutions through sustained interaction with execution environments. Hierarchical agentic SFT develops general tool-use and domain-specific optimization skills, while **Windowed Interaction Reinforcement Learning (WIRL)** trains multi-turn decisions using feedback from completed experiments.
 ### Recommended Sampling Parameters
+| Parameter              | Value |
+| :--------------------- | ----: |
+| `temperature`        |   1.0 |
+| `top_p`              |  0.95 |
+| `top_k`              |    20 |
+| `min_p`              |   0.0 |
+| `presence_penalty`   |   1.5 |
+| `repetition_penalty` |   1.0 |
+These parameters apply to both thinking and non-thinking modes. Set `chat_template_kwargs.enable_thinking` to `true` to enable thinking or `false` to disable it. Machine-readable profiles are provided in [inference_profiles.json](./inference_profiles.json).