Instructions to use hyunseoki/memrag-mem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyunseoki/memrag-mem with Transformers:

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

pipe = pipeline("image-text-to-text", model="hyunseoki/memrag-mem")
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("hyunseoki/memrag-mem")
model = AutoModelForMultimodalLM.from_pretrained("hyunseoki/memrag-mem")
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 hyunseoki/memrag-mem with vLLM:

Install from pip and serve model

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

SGLang

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

memrag-mem — Trajectory-Memory RAG (GUI agent)

Cold-start SFT from Qwen3.5-4B for GUI next-action prediction. This checkpoint = the retrieved trajectory memory (main result) arm of a 3-arm A/B.

Action accuracy (n=498 test, AgentNetBench score_pair): 0.556 — +19.0pp vs basecur, +8.6pp vs basefull; usage-gap +11.4pp (memory is genuinely used)

arm	action acc (n=498)
basecur (current only)	0.366
basefull (full history)	0.470
mem (retrieved memory)	0.556

Status: v1, single-seed (positive; 3-seed confirmation pending). See the collection for the other arms.

Load

from transformers import AutoProcessor
from qwen_cua.modeling_qwen35_vl_latent import Qwen35VLLatentForConditionalGeneration as M
proc  = AutoProcessor.from_pretrained("hyunseoki/memrag-mem", max_pixels=1_000_000)
model = M.from_pretrained("hyunseoki/memrag-mem", torch_dtype="bfloat16", attn_implementation="flash_attention_2")

Plain Qwen3.5-VL arch (wm.enabled=false) — also loadable with the standard class.