Instructions to use hmhm1229/MoRE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hmhm1229/MoRE with Transformers:

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

pipe = pipeline("image-text-to-text", model="hmhm1229/MoRE")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("hmhm1229/MoRE")
model = AutoModelForImageTextToText.from_pretrained("hmhm1229/MoRE")
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 hmhm1229/MoRE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hmhm1229/MoRE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hmhm1229/MoRE",
		"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/hmhm1229/MoRE

SGLang

How to use hmhm1229/MoRE 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 "hmhm1229/MoRE" \
    --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": "hmhm1229/MoRE",
		"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 "hmhm1229/MoRE" \
        --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": "hmhm1229/MoRE",
		"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 hmhm1229/MoRE with Docker Model Runner:
```
docker model run hf.co/hmhm1229/MoRE
```

hmhm1229 commited on Apr 28

Commit

d1f1c71

verified ·

1 Parent(s): 4d9f1af

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -37,7 +37,8 @@ Maosong Sun<sup>2</sup>
 ## News
-8.22 We upload [MoRE-3B](https://huggingface.co/hmhm1229/R1-Router-3B).
 ## Environment
 For training, answer generation, and evaluation processes:
@@ -61,7 +62,7 @@ conda activate retriever
 wikiextractor enwiki-20241020-pages-articles-multistream.xml.bz2 -o wiki_extracted
 python wiki_preprocess.py
 ```
-For the image corpus, you can directly download [M-BEIR](https://huggingface.co/datasets/TIGER-Lab/M-BEIR). To embed and index it you can follow the [repository](https://github.com/TIGER-AI-Lab/UniIR)
 For the table corpus, you can download, embed and index Open-WikiTable following the [repository](https://github.com/sean0042/Open_WikiTable), or you can download directly the one we have already preprocessed from [here](https://huggingface.co/hmhm1229/table-retriever).

 ## News
+26.04.28 Our Work is now accepted by SIGIR 2026🎉!
+8.22 We upload [MoRE-3B](https://huggingface.co/hmhm1229/MoRE-3B).
 ## Environment
 For training, answer generation, and evaluation processes:
 wikiextractor enwiki-20241020-pages-articles-multistream.xml.bz2 -o wiki_extracted
 python wiki_preprocess.py
 ```
+For the image corpus, you can directly download [M-BEIR](https://huggingface.co/datasets/TIGER-Lab/M-BEIR). To embed and index it, you can follow the [repository](https://github.com/TIGER-AI-Lab/UniIR)
 For the table corpus, you can download, embed and index Open-WikiTable following the [repository](https://github.com/sean0042/Open_WikiTable), or you can download directly the one we have already preprocessed from [here](https://huggingface.co/hmhm1229/table-retriever).