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
Update README.md
Browse files
README.md
CHANGED
|
@@ -47,6 +47,6 @@ This repo is used to open-source all the experts in MLLM-CL experiments, includi
|
|
| 47 |
Please post an issue in our Github.
|
| 48 |
|
| 49 |
## About us: MLLM-CL Community
|
| 50 |
-

|
| 51 |
We are the members from MLLM-CL, an open-source community focus on Continual learning of Multimodal Large Language Models.
|
| 52 |
If you are interested in our community, feel free to contact us in github or email.
|