Model reference · open weights
CheXagent-2-srrg-impression is an open-weight language model from StanfordAIMI. CheXagent-2-3b-srrg-impression (FP32) weighs 6.3 GB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the StanfordAIMI/CheXagent-2-3b-srrg-impression model card, 2026-10-01
What it is
| Released by | StanfordAIMI |
|---|---|
| Released | 2024-08-12 |
| Parameters | 3.1B |
| VRAM | 6.3 GB for the weights |
What it runs on
How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB | fits |
| RTX 4060 Ti 16 GB | fits |
| RTX 3090 24 GB | fits |
| RTX 4090 24 GB | fits |
| RTX 5090 32 GB | fits |
| L40S 48 GB | fits |
| A100 80 GB | fits |
| H100 80 GB | fits |
| RTX PRO 6000 Blackwell 96 GB | fits |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB | fits |
| B200 180 GB | fits |
From the model card
Requirements:
pip install opencv-python
pip install albumentations
pip install accelerate
torch==2.2.1
transformers==4.39.0 # may work with more recent version
Adapted sample script for SRRG
import io
import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer
import tempfile
# step 1: Setup constants
model_name = "StanfordAIMI/CheXagent-2-3b-srrg-impression"
dtype = torch.bfloat16
device = "cuda"
# step 2: Load Processor and Model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
model = model.to(dtype)
model.eval()
# step 3: Download image from URL, save to a local file, and prepare path list
url = "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"
resp = requests.get(url)
resp.raise_for_status()
# Use a NamedTemporaryFile so it lives on disk
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmpfile:
tmpfile.write(resp.content)
local_path = tmpfile.name # this is a real file path on disk
paths = [local_path]
prompt = "Structured Radiology Report Generation for Impression Section"
# build the multimodal input
query = tokenizer.from_list_format(
[*([{"image": img} for img in paths]), {"text": prompt}]
)
# format as a chat conversation
conv = [
{"from": "system", "value": "You are a helpful assistant."},
{"from": "human", "value": query},
]
# tokenize and generate
input_ids = tokenizer.apply_chat_template(
conv, add_generation_prompt=True, return_tensors="pt"
)
output = model.generate(
input_ids.to(device),
do_sample=False,
num_beams=1,
temperature=1.0,
top_p=1.0,
use_cache=True,
max_new_tokens=512,
)[0]
# decode the “impression” text
response = tokenizer.decode(output[input_ids.size(1) : -1])
print(response)
Response:
1. Interval increase in bilateral pleural effusions.
2. Interval increase in bibasilar opacities, which may represent atelectasis or consolidation.
3. Stable position of the right upper extremity peripherally inserted central catheter (PICC line).
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works