Image-Text-to-Text
Transformers
Safetensors
English
pathology
computational-pathology
digital-pathology
histopathology
whole-slide-image
vision-language-model
report-generation
synoptic-report
case-level
conch
qwen2.5
Eval Results (legacy)
Instructions to use AtlasAnalyticsLab/PathoSynVLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AtlasAnalyticsLab/PathoSynVLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="AtlasAnalyticsLab/PathoSynVLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AtlasAnalyticsLab/PathoSynVLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AtlasAnalyticsLab/PathoSynVLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AtlasAnalyticsLab/PathoSynVLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AtlasAnalyticsLab/PathoSynVLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AtlasAnalyticsLab/PathoSynVLM
- SGLang
How to use AtlasAnalyticsLab/PathoSynVLM 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 "AtlasAnalyticsLab/PathoSynVLM" \ --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": "AtlasAnalyticsLab/PathoSynVLM", "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 "AtlasAnalyticsLab/PathoSynVLM" \ --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": "AtlasAnalyticsLab/PathoSynVLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AtlasAnalyticsLab/PathoSynVLM with Docker Model Runner:
docker model run hf.co/AtlasAnalyticsLab/PathoSynVLM
| { | |
| "name": "PathoSynVLM", | |
| "results": [ | |
| { | |
| "task": { | |
| "type": "image-text-to-text", | |
| "name": "Case-level pathology synoptic report generation" | |
| }, | |
| "dataset": { | |
| "name": "HISTAI case-report pairs", | |
| "type": "histai/HISTAI-metadata" | |
| }, | |
| "metrics": [ | |
| { | |
| "type": "rouge", | |
| "name": "ROUGE-L", | |
| "value": 0.2495 | |
| }, | |
| { | |
| "type": "meteor", | |
| "name": "METEOR", | |
| "value": 0.1988 | |
| }, | |
| { | |
| "type": "bleu", | |
| "name": "BLEU-4", | |
| "value": 0.0525 | |
| }, | |
| { | |
| "type": "bertscore", | |
| "name": "BERTScore F1", | |
| "value": 0.3018 | |
| }, | |
| { | |
| "type": "accuracy", | |
| "name": "Diagnosis Exact", | |
| "value": 0.1667 | |
| }, | |
| { | |
| "type": "accuracy", | |
| "name": "Diagnosis Relaxed", | |
| "value": 0.3333 | |
| }, | |
| { | |
| "type": "accuracy", | |
| "name": "Certainty", | |
| "value": 0.9 | |
| } | |
| ] | |
| } | |
| ] | |
| } | |