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
#3
by nielsr HF Staff - opened
This PR aims to improve the model card by:
- Updating the
pipeline_tagfromvisual-question-answeringtoimage-text-to-textto better reflect the model's capabilities as a Multimodal Large Language Model. - Adding
MLLM-CL/MLLM-CL-ReplayDatato thedatasetsmetadata, as referenced in the project's GitHub README. - Including the Hugging Face paper link alongside the existing arXiv link for improved accessibility to the paper.
These changes enhance the model's discoverability and provide more comprehensive information for users on the Hugging Face Hub.
z-hb changed pull request status to merged