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
File size: 1,448 Bytes
aa50ff8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"best_step": 30400,
"best_epoch": 7,
"best_source": "step_eval",
"best_val_loss": 1.0108920872211455,
"metrics": {
"val_loss": 1.0108920872211455,
"val_n_samples": 30,
"val/vision_tokens_total": 34833,
"val/text_tokens_total": 9942,
"val/target_tokens_total": 3305,
"val/wsi_count_total": 188,
"val/avg_vision_tokens_per_sample": 1161.1,
"val/avg_text_tokens_per_sample": 331.4,
"val/avg_target_tokens_per_sample": 110.16666666666667,
"val/avg_wsi_per_sample": 6.266666666666667,
"val_bertscore_f1": 0.3017574870338043,
"val/field_eval_n": 30,
"val_diagnosis_relaxed_match_rate": 0.3333333333333333,
"val_diagnosis_exact_match_rate": 0.16666666666666666,
"val_certainty_match_rate": 0.9,
"val_certainty_exact_match_rate": 0.9,
"val_conclusion_exact_match_rate": 0.0,
"val_conclusion_rougeL": 0.24946812913260996,
"val_conclusion_meteor": 0.1988453811627603,
"val_conclusion_bleu4": 5.251211118089842,
"val/pred_format_score_0_count": 0,
"val/ref_format_score_0_count": 0,
"val/pred_format_score_1_count": 0,
"val/ref_format_score_1_count": 0,
"val/pred_format_score_2_count": 0,
"val/ref_format_score_2_count": 0,
"val/pred_format_score_3_count": 30,
"val/ref_format_score_3_count": 30,
"val/pred_diagnosis_present_rate": 1.0,
"val/pred_certainty_present_rate": 1.0,
"val/pred_conclusion_present_rate": 1.0
}
} |