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
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| <title id="title">PathoSynVLM reported results</title> | |
| <desc id="desc">Stage 1 visual-language alignment and Stage 2 HISTAI case-level report generation metrics. All bars share a zero-to-one scale and exact values appear in separate labels.</desc> | |
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| <text x="60" y="45" class="eyebrow">PATHOSYNVLM</text> | |
| <text x="60" y="82" class="title">Reported Results</text> | |
| <text x="60" y="109" class="subtitle">Headline paper metrics. Every bar uses the same 0-1 scale; exact values sit in a dedicated column.</text> | |
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| <text x="95" y="181" class="panel-title">Stage 1 · VL alignment</text> | |
| <text x="95" y="209" class="panel-subtitle">HistGen + REG2025 · trainable projector only</text> | |
| <text x="95" y="259" class="group">TEXT GENERATION</text> | |
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| <text x="95" y="302" class="metric">ROUGE-L</text> | |
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| <text x="575" y="298" text-anchor="middle" class="value">0.4743</text> | |
| <text x="95" y="370" class="metric">METEOR</text> | |
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| <text x="575" y="366" text-anchor="middle" class="value">0.4810</text> | |
| <text x="95" y="438" class="metric">BLEU-4</text> | |
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| <text x="575" y="434" text-anchor="middle" class="value">0.1247</text> | |
| <text x="95" y="506" class="metric">BERTScore F1</text> | |
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| <text x="575" y="502" text-anchor="middle" class="value">0.4253</text> | |
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| <text x="725" y="209" class="panel-subtitle">Case-level structured report generation</text> | |
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| <text x="725" y="286" class="metric">ROUGE-L</text> | |
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| <text x="1257" y="284" text-anchor="middle" class="value">0.2495</text> | |
| <text x="725" y="326" class="metric">METEOR</text> | |
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| <text x="1257" y="324" text-anchor="middle" class="value">0.1988</text> | |
| <text x="725" y="366" class="metric">BLEU-4</text> | |
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| <text x="725" y="406" class="metric">BERTScore F1</text> | |
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| <text x="1257" y="488" text-anchor="middle" class="value">0.1667</text> | |
| <text x="725" y="530" class="metric">Diagnosis · relaxed</text> | |
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| <text x="1257" y="528" text-anchor="middle" class="value">0.3333</text> | |
| <text x="725" y="570" class="metric">Certainty · exact</text> | |
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| <text x="1257" y="568" text-anchor="middle" class="value">0.9000</text> | |
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