Instructions to use FrontisAI/Frontis-MA1-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontisAI/Frontis-MA1-35B with Transformers:

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

pipe = pipeline("image-text-to-text", model="FrontisAI/Frontis-MA1-35B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FrontisAI/Frontis-MA1-35B")
model = AutoModelForMultimodalLM.from_pretrained("FrontisAI/Frontis-MA1-35B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrontisAI/Frontis-MA1-35B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontisAI/Frontis-MA1-35B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontisAI/Frontis-MA1-35B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FrontisAI/Frontis-MA1-35B

SGLang

How to use FrontisAI/Frontis-MA1-35B 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 "FrontisAI/Frontis-MA1-35B" \
    --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": "FrontisAI/Frontis-MA1-35B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "FrontisAI/Frontis-MA1-35B" \
        --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": "FrontisAI/Frontis-MA1-35B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use FrontisAI/Frontis-MA1-35B with Docker Model Runner:
```
docker model run hf.co/FrontisAI/Frontis-MA1-35B
```

abab112 commited on Jul 31

Commit

79a29e4

verified ·

1 Parent(s): 376bfca

Align release navigation and paper links

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -29,13 +29,13 @@ tags:
 </p>
 <p align="center">
-  <a href="#paper-and-citation">📄 Paper</a>
   &nbsp;•&nbsp;
-  <a href="https://github.com/FrontisAI/OpenRSI">💻 Code</a>
   &nbsp;•&nbsp;
-  <a href="https://rc314159-creator.github.io/OpenRSI/">🌐 Project</a>
   &nbsp;•&nbsp;
-  <a href="https://huggingface.co/FrontisAI">🤗 Models</a>
   &nbsp;•&nbsp;
   <a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks">🧩 Tasks</a>
   &nbsp;•&nbsp;
@@ -270,7 +270,7 @@ This repository is the canonical BF16 Transformers release. The [Frontis-MA1-35B
 <a id="paper"></a>
 ## Paper and citation
-**OpenMLE: Scaling Evolutionary MLE Agents toward Recursive Self-Improvement**
 Preprint: [arXiv:2607.28568](https://arxiv.org/abs/2607.28568).

 </p>
 <p align="center">
+  <a href="https://arxiv.org/abs/2607.28568">📄 Paper</a>
   &nbsp;•&nbsp;
+  <a href="https://frontisai.github.io/OpenRSI/">🌐 Project</a>
   &nbsp;•&nbsp;
+  <a href="https://github.com/FrontisAI/OpenRSI">💻 Code</a>
   &nbsp;•&nbsp;
+  <a href="https://huggingface.co/collections/FrontisAI/frontis-ma1">🤗 Models</a>
   &nbsp;•&nbsp;
   <a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks">🧩 Tasks</a>
   &nbsp;•&nbsp;
 <a id="paper"></a>
 ## Paper and citation
+**Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering**
 Preprint: [arXiv:2607.28568](https://arxiv.org/abs/2607.28568).