Image-Text-to-Text
Transformers
English
finance
medical
AD
MLLM-CL
Sci
RS
Math
OCR
Count
GUI-Agent
DCL
ACL
llava
multimodal
image-to-text
text-generation
Instructions to use MLLM-CL/MRLoRA_Experts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLLM-CL/MRLoRA_Experts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MLLM-CL/MRLoRA_Experts")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLLM-CL/MRLoRA_Experts", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MLLM-CL/MRLoRA_Experts with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MLLM-CL/MRLoRA_Experts" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLLM-CL/MRLoRA_Experts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MLLM-CL/MRLoRA_Experts
- SGLang
How to use MLLM-CL/MRLoRA_Experts 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 "MLLM-CL/MRLoRA_Experts" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLLM-CL/MRLoRA_Experts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "MLLM-CL/MRLoRA_Experts" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLLM-CL/MRLoRA_Experts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MLLM-CL/MRLoRA_Experts with Docker Model Runner:
docker model run hf.co/MLLM-CL/MRLoRA_Experts
Improve model card: Update pipeline tag, add dataset, and HF paper link
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by nielsr HF Staff - opened
README.md
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language:
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metrics:
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- accuracy
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base_model_relation: adapter
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tags:
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- finance
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- medical
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- multimodal
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library_name: transformers
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datasets:
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## MLLM-CL Benchmark Description
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whereas the latter evaluates on non-IID scenarios with emerging model ability.
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For more details, please refer to:
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**MLLM-CL: Continual Learning for Multimodal Large Language Models** [[paper](https://arxiv.org/abs/2506.05453)], [[code](https://github.com/bjzhb666/MLLM-CL/)].
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[Hongbo Zhao](https://scholar.google.com/citations?user=Gs22F0UAAAAJ&hl=zh-CN), [Fei Zhu](https://impression2805.github.io/), [Haiyang Guo](https://ghy0501.github.io/guohaiyang0501.github.io/), [Meng Wang](https://moenupa.github.io/), Rundong Wang, [Gaofeng Meng](https://scholar.google.com/citations?hl=zh-CN&user=5hti_r0AAAAJ), [Zhaoxiang Zhang](https://scholar.google.com/citations?hl=zh-CN&user=qxWfV6cAAAAJ)
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base_model:
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- OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B
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datasets:
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- MLLM-CL/MLLM-CL
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- MLLM-CL/MLLM-CL-ReplayData
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language:
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- en
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library_name: transformers
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license: apache-2.0
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metrics:
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- accuracy
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pipeline_tag: image-text-to-text
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tags:
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- finance
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- medical
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- multimodal
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- image-to-text
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- text-generation
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base_model_relation: adapter
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---
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## MLLM-CL Benchmark Description
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whereas the latter evaluates on non-IID scenarios with emerging model ability.
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For more details, please refer to:
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**MLLM-CL: Continual Learning for Multimodal Large Language Models** [[paper](https://arxiv.org/abs/2506.05453)], [[HF paper](https://huggingface.co/papers/2506.05453)], [[code](https://github.com/bjzhb666/MLLM-CL/)].
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[Hongbo Zhao](https://scholar.google.com/citations?user=Gs22F0UAAAAJ&hl=zh-CN), [Fei Zhu](https://impression2805.github.io/), [Haiyang Guo](https://ghy0501.github.io/guohaiyang0501.github.io/), [Meng Wang](https://moenupa.github.io/), Rundong Wang, [Gaofeng Meng](https://scholar.google.com/citations?hl=zh-CN&user=5hti_r0AAAAJ), [Zhaoxiang Zhang](https://scholar.google.com/citations?hl=zh-CN&user=qxWfV6cAAAAJ)
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