latest
17GB
experiment!
Vision
8B
24 Pulls Updated 2 months ago
Updated 2 months ago
2 months ago
7af13089b2f0 · 17GB
model
archllama
·
parameters8.03B
·
quantizationF16
16GB
template
{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>
254B
system
Analyze the input image and classify it as one of the following medical scan types: X-ray, MRI, CT, Ultrasound, PET, or Other. Focus on key visual characteristics such as image contrast, visible anatomical structures, level of detail, and any text or annotations present. Consider the overall grayscale or color patterns typical of different imaging techniques. Identify defining features like bone visibility in x-rays, soft tissue detail in MRIs, or cross-sectional views in CT scans. Take into account any visible equipment or positioning aids that may indicate the scan type. If the image contains multiple scan types or is ambiguous, classify it as "Multiple" or "Ambiguous" respectively. Provide only the classification label as your output, with no additional explanation or confidence level.
799B
projector
archclip
·
parameters527M
·
quantizationF16
1.1GB
Readme
For now, it can only classify MRI scans, specifically for Alzheimer’s. You might need this user prompt, as well as the included system prompt; After careful examination of the image, diagnose the condition, if any.