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 metadata
#2
by Moenupa - opened
README.md
CHANGED
|
@@ -7,6 +7,7 @@ metrics:
|
|
| 7 |
base_model:
|
| 8 |
- llava-hf/llava-1.5-7b-hf
|
| 9 |
- OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B
|
|
|
|
| 10 |
tags:
|
| 11 |
- finance
|
| 12 |
- medical
|
|
@@ -20,8 +21,14 @@ tags:
|
|
| 20 |
- GUI-Agent
|
| 21 |
- DCL
|
| 22 |
- ACL
|
|
|
|
|
|
|
|
|
|
|
|
|
| 23 |
pipeline_tag: visual-question-answering
|
| 24 |
library_name: transformers
|
|
|
|
|
|
|
| 25 |
---
|
| 26 |
|
| 27 |
## MLLM-CL Benchmark Description
|
|
|
|
| 7 |
base_model:
|
| 8 |
- llava-hf/llava-1.5-7b-hf
|
| 9 |
- OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B
|
| 10 |
+
base_model_relation: adapter
|
| 11 |
tags:
|
| 12 |
- finance
|
| 13 |
- medical
|
|
|
|
| 21 |
- GUI-Agent
|
| 22 |
- DCL
|
| 23 |
- ACL
|
| 24 |
+
- llava
|
| 25 |
+
- multimodal
|
| 26 |
+
- image-to-text
|
| 27 |
+
- text-generation
|
| 28 |
pipeline_tag: visual-question-answering
|
| 29 |
library_name: transformers
|
| 30 |
+
datasets:
|
| 31 |
+
- MLLM-CL/MLLM-CL
|
| 32 |
---
|
| 33 |
|
| 34 |
## MLLM-CL Benchmark Description
|