{"id":8189,"date":"2024-09-11T13:42:51","date_gmt":"2024-09-11T17:42:51","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/minervalab\/?page_id=8189"},"modified":"2026-08-24T16:43:43","modified_gmt":"2026-08-24T20:43:43","slug":"ollama","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/minervalab\/documentation\/ollama\/","title":{"rendered":"Ollama"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;0px||0px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row admin_label=&#8221;row&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;||0px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text admin_label=&#8221;Breadcrumb&#8221; _builder_version=&#8221;4.16&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><a href=\"https:\/\/labs.icahn.mssm.edu\/minervalab\/scientific-computing-and-data\/\">Scientific Computing and Data<\/a>\u00a0\/\u00a0<a href=\"https:\/\/labs.icahn.mssm.edu\/minervalab\/\">High Performance Computing<\/a> \/ <a href=\"https:\/\/labs.icahn.mssm.edu\/minervalab\/documentation\/\">Documentation<\/a> \/ Ollama<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; hover_enabled=&#8221;0&#8243; custom_css_free_form=&#8221;.minerva-doc {||  &#8211;ink-900: #212070;||  &#8211;ink-700: #3a3a52;||  &#8211;ink-500: #6f7086;||  &#8211;line: #e4e4ee;||  &#8211;line-soft: #efeff6;||  &#8211;bg-page: #fbfbfd;||  &#8211;bg-card: #ffffff;||  &#8211;bg-subtle: #f5f5fa;||  &#8211;accent-600: #dc298d;||  &#8211;accent-500: #dc298d;||  &#8211;accent-100: #fbdeee;||  &#8211;accent-050: #fdf0f7;||  &#8211;indigo-100: #e4e4f2;||  &#8211;indigo-050: #f2f2f9;||  &#8211;code-bg: #1b1b3a;||  &#8211;code-text: #e6e6f0;||  &#8211;code-line: rgba(255, 255, 255, 0.08);||  &#8211;font-body: %22Open Sans%22, -apple-system, BlinkMacSystemFont, %22Segoe UI%22, Helvetica, Arial, sans-serif;||  &#8211;font-mono: %22SFMono-Regular%22, %22JetBrains Mono%22, Consolas, Menlo, monospace;||  &#8211;radius-lg: 14px;||  &#8211;radius-md: 8px;||  width: 100%;||  padding: 0 8px;||  background: var(&#8211;bg-page);||  color: var(&#8211;ink-700);||  font-family: var(&#8211;font-body);||  font-size: 16px;||  line-height: 1.65;||  -webkit-font-smoothing: antialiased;||}||||.minerva-doc *,||.minerva-doc *::before,||.minerva-doc *::after {||  box-sizing: border-box;||}||||.minerva-doc p br,||.minerva-doc li br,||.minerva-doc .note br,||.minerva-doc .callout br {||  display: none;||}||||.minerva-doc .site-header {||  padding: 40px 0 8px;||}||||.minerva-doc .brand-tag {||  display: inline-flex;||  align-items: center;||  font-size: 12.5px;||  font-weight: 600;||  color: #212070;||  background: var(&#8211;indigo-100);||  padding: 5px 12px;||  border-radius: 999px;||  letter-spacing: 0.2px;||  margin-bottom: 14px;||}||||.minerva-doc .brand-title {||  margin: 0;||  color: var(&#8211;ink-900);||  font-size: 32px;||  font-weight: 700;||  letter-spacing: -0.6px;||  line-height: 1.2;||}||||.minerva-doc .intro {||  padding: 14px 0 30px;||  border-bottom: 1px solid var(&#8211;line);||}||||.minerva-doc .doc-section {||  border-bottom: 1px solid var(&#8211;line);||}||||.minerva-doc .doc-section:last-of-type {||  border-bottom: none;||}||||.minerva-doc .doc-section summary {||  list-style: none;||  cursor: pointer;||  padding: 22px 0;||  display: flex;||  align-items: baseline;||  gap: 8px;||  font-size: 26px;||  font-weight: 700;||  color: var(&#8211;ink-900);||  position: relative;||}||||.minerva-doc .doc-section summary::-webkit-details-marker {||  display: none;||}||||.minerva-doc .doc-section summary::after {||  content: %22%22;||  margin-left: auto;||  align-self: center;||  width: 10px;||  height: 10px;||  border-right: 2px solid var(&#8211;ink-500);||  border-bottom: 2px solid var(&#8211;ink-500);||  transform: rotate(45deg);||  transition: transform 0.2s ease;||  flex: none;||}||||.minerva-doc .doc-section%91open%93 summary::after {||  transform: rotate(-135deg);||  border-color: var(&#8211;accent-600);||}||||.minerva-doc .doc-section summary:hover {||  color: var(&#8211;accent-600);||}||||.minerva-doc .section-body {||  padding: 16px 0 28px;||}||||.minerva-doc .intro-eyebrow {||  display: block;||  font-family: var(&#8211;font-body);||  font-size: 26px;||  font-weight: 700;||  color: var(&#8211;ink-900);||  letter-spacing: 0;||  margin-bottom: 10px;||}||||.minerva-doc p {||  margin: 0 0 16px;||  color: var(&#8211;ink-700);||}||||.minerva-doc a {||  color: var(&#8211;accent-600);||  text-decoration: none;||  background-image: linear-gradient(var(&#8211;accent-500), var(&#8211;accent-500));||  background-repeat: no-repeat;||  background-size: 100% 1px;||  background-position: 0 100%;||}||||.minerva-doc a:hover {||  color: var(&#8211;ink-900);||  background-image: linear-gradient(#212070, #212070);||}||||.minerva-doc strong {||  font-weight: 600;||  color: var(&#8211;ink-900);||}||||.minerva-doc ul,||.minerva-doc ol {||  margin: 0 0 16px;||  padding-left: 22px;||}||||.minerva-doc li {||  margin-bottom: 9px;||}||||.minerva-doc li ul,||.minerva-doc li ol {||  margin-top: 9px;||  margin-bottom: 0;||}||||.minerva-doc ul &gt; li::marker {||  color: var(&#8211;accent-500);||}||||.minerva-doc ol &gt; li::marker {||  color: var(&#8211;accent-500);||  font-weight: 600;||}||||.minerva-doc .callout {||  margin: 20px 0 16px;||  padding: 18px 22px;||  border-radius: var(&#8211;radius-md);||}||||.minerva-doc .callout-accent {||  background: var(&#8211;indigo-050);||  border: 1px solid var(&#8211;indigo-100);||  border-left: 3px solid var(&#8211;accent-500);||}||||.minerva-doc code {||  font-family: var(&#8211;font-mono);||  background: var(&#8211;bg-subtle);||  color: #b81f77;||  padding: 2px 6px;||  border-radius: 4px;||  font-size: 0.87em;||  border: 1px solid var(&#8211;line);||}||||.minerva-doc .code-block {||  background: var(&#8211;code-bg);||  border-radius: var(&#8211;radius-md);||  margin: 14px 0 22px;||  overflow: hidden;||  box-shadow: 0 8px 24px rgba(33, 32, 112, 0.18);||}||||.minerva-doc .code-block-header {||  display: flex;||  align-items: center;||  gap: 6px;||  padding: 8px 10px 8px 14px;||  border-bottom: 1px solid var(&#8211;code-line);||}||||.minerva-doc .code-lang {||  font-family: var(&#8211;font-mono);||  font-size: 11px;||  color: #9b9bc0;||  text-transform: uppercase;||  letter-spacing: 1px;||}||||.minerva-doc .code-block pre {||  margin: 0;||  padding: 16px 18px 18px;||  font-family: var(&#8211;font-mono);||  font-size: 13.5px;||  line-height: 1.7;||  color: var(&#8211;code-text);||  white-space: pre-wrap;||  word-break: break-word;||  overflow-x: auto;||}||||.minerva-doc .code-block pre code {||  background: none;||  border: none;||  padding: 0;||  color: inherit;||}||||.minerva-doc .table-wrap {||  overflow-x: auto;||  margin: 14px 0 16px;||  border-radius: var(&#8211;radius-md);||  border: 1px solid var(&#8211;line);||}||||.minerva-doc table {||  width: 100%;||  border-collapse: collapse;||  font-size: 14px;||}||||.minerva-doc th {||  background: var(&#8211;indigo-050);||  color: var(&#8211;ink-900);||  font-weight: 600;||  text-align: left;||  padding: 12px 16px;||  font-size: 12px;||  text-transform: uppercase;||  letter-spacing: 0.5px;||  border-bottom: 1px solid var(&#8211;indigo-100);||}||||.minerva-doc td {||  padding: 12px 16px;||  border-top: 1px solid var(&#8211;line-soft);||  vertical-align: top;||  color: var(&#8211;ink-700);||}||||.minerva-doc tbody tr:hover {||  background: var(&#8211;bg-subtle);||}||||.minerva-doc .sub-section {||  border-top: 1px solid var(&#8211;line-soft);||  margin-top: 6px;||}||||.minerva-doc .sub-section:first-of-type {||  border-top: none;||  margin-top: 0;||}||||.minerva-doc .sub-section summary {||  list-style: none;||  cursor: pointer;||  padding: 14px 0;||  display: flex;||  align-items: baseline;||  gap: 8px;||  font-size: 15px;||  font-weight: 600;||  color: var(&#8211;ink-900);||  position: relative;||}||||.minerva-doc .sub-section summary::-webkit-details-marker {||  display: none;||}||||.minerva-doc .sub-section summary::after {||  content: %22%22;||  margin-left: auto;||  align-self: center;||  width: 8px;||  height: 8px;||  border-right: 2px solid var(&#8211;ink-500);||  border-bottom: 2px solid var(&#8211;ink-500);||  transform: rotate(45deg);||  transition: transform 0.2s ease;||  flex: none;||}||||.minerva-doc .sub-section%91open%93 summary::after {||  transform: rotate(-135deg);||  border-color: var(&#8211;accent-600);||}||||.minerva-doc .sub-section summary:hover {||  color: var(&#8211;accent-600);||}||||.minerva-doc .sub-body {||  padding: 0 0 20px;||}||||.minerva-doc .sub-body &gt; *:last-child {||  margin-bottom: 0;||}||||.minerva-doc .doc-footer {||  padding: 24px 0 8px;||  text-align: center;||  color: var(&#8211;ink-500);||  font-size: 13px;||}||||@media (max-width: 520px) {||  .minerva-doc .brand-title { font-size: 24px; }||  .minerva-doc .doc-section summary { font-size: 21px; }||  .minerva-doc .intro-eyebrow { font-size: 21px; }||}&#8221; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<div class=\"minerva-doc\">\n<div class=\"site-header\">\n<p><span class=\"brand-tag\">HPC Documentation<\/span><\/p>\n<h1 class=\"brand-title\">Generative AI Assistant Tool on Minerva<\/h1>\n<\/div>\n<div class=\"intro\">\n<p><span class=\"intro-eyebrow\">Overview<\/span><br \/>\nThe generative AI Assistant tool on Minerva (<a href=\"https:\/\/assistant.hpc.mssm.edu\/\">https:\/\/assistant.hpc.mssm.edu\/<\/a>) is an advanced AI-powered platform designed to support clinicians, researchers, data scientists, and students in their daily work. It enables users to interact with cutting-edge Large Language Models (LLMs) built on leading open-source technologies.The tool is deployed on the <strong>Minerva High Performance Computing (HPC)<\/strong> infrastructure. Minerva provides powerful computing resources, ensuring fast and efficient performance for a wide range of research and clinical applications.<\/p>\n<ul>\n<li><strong>Backend:<\/strong> Powered by an <strong>Ollama<\/strong> instance, currently running on two NVIDIA H100 GPUs (80 GB memory each).<\/li>\n<li><strong>Frontend:<\/strong> Developed using <strong>Open WebUI<\/strong>, providing users with a seamless and intuitive experience.<\/li>\n<\/ul>\n<\/div>\n<p><!-- Section 1 --><\/p>\n<details class=\"doc-section\" open=\"\">\n<summary>1. Key Features<\/summary>\n<div class=\"section-body\">\n<ul>\n<li><strong>Fast Inference:<\/strong> Accelerated by Minerva&#8217;s high-performance GPU infrastructure.<\/li>\n<li><strong>Retrieval-Augmented Generation (RAG):<\/strong> Enables context-aware, document-grounded answers.<\/li>\n<li><strong>Multi-User Support:<\/strong> Concurrent access with dedicated GPU session management.<\/li>\n<li><strong>Web Interface:<\/strong> Intuitive user interactions via Open WebUI.<\/li>\n<li><strong>Scalable Backend:<\/strong> Dynamic GPU expansion based on user demand.<\/li>\n<li><strong>Multimodal Support:<\/strong> Handles both natural language and code tasks.<\/li>\n<li><strong>API Access:<\/strong> Programmatic integration with external tools and pipelines.<\/li>\n<li><strong>Secure &amp; Private:<\/strong> Fully internal to Mount Sinai with no external data sharing.<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<p><!-- Section 2: Reference to Models Page --><\/p>\n<details class=\"doc-section\" open=\"\">\n<summary>2. Supported Models<\/summary>\n<div class=\"section-body\">\n<p>A comprehensive inventory of supported open-source LLMs\u2014including model parameter sizes, quantization levels, best use cases, and official source links\u2014is maintained on our dedicated models reference page.<\/p>\n<div class=\"callout callout-accent\"><strong>View Full Model Catalog:<\/strong> For detailed technical specifications across all available Gemma, Llama, Nemotron, Phi, and multimodal models, visit the <a href=\"https:\/\/labs.icahn.mssm.edu\/minervalab\/documentation\/ollama\/ai-models\/\">Supported AI Models Reference Guide \u2192<\/a><\/div>\n<\/div>\n<\/details>\n<p><!-- Section 3 --><\/p>\n<details class=\"doc-section\">\n<summary>3. Capabilities Overview<\/summary>\n<div class=\"section-body\">\n<p>Using the Assistant Tool, users can perform tasks such as:<\/p>\n<ol>\n<li>Asking scientific questions<\/li>\n<li>Summarizing and chatting with research papers<\/li>\n<li>Getting explanations of medical terms<\/li>\n<li>Obtaining programming assistance<\/li>\n<li>Translating text between languages<\/li>\n<li>Comparing multiple models simultaneously<\/li>\n<li>Accessing functionality via API endpoints<\/li>\n<\/ol>\n<\/div>\n<\/details>\n<p><!-- Section 4 --><\/p>\n<details class=\"doc-section\">\n<summary>4. Accessing the Assistant Tool<\/summary>\n<div class=\"section-body\">\n<ol>\n<li>Open any modern web browser (Chrome, Firefox, Safari, or Edge).<\/li>\n<li>Navigate to: <a href=\"https:\/\/assistant.hpc.mssm.edu\" target=\"_blank\" rel=\"noopener\">https:\/\/assistant.hpc.mssm.edu<\/a><\/li>\n<li>On the login page, click the <strong>&#8220;Continue with Microsoft&#8221;<\/strong> button.<\/li>\n<li>Authenticate using your <strong>Mount Sinai credentials<\/strong>.<\/li>\n<\/ol>\n<\/div>\n<\/details>\n<p><!-- Section 5 --><\/p>\n<details class=\"doc-section\">\n<summary>5. Interface Guide<\/summary>\n<div class=\"section-body\">\n<details class=\"sub-section\">\n<summary>1. Left Navigation Sidebar<\/summary>\n<div class=\"sub-body\">\n<ul>\n<li><strong>Open WebUI Header &amp; Sidebar Toggle (<code>[|]<\/code>):<\/strong> Expand or collapse the sidebar menu.<\/li>\n<li><strong>New Chat:<\/strong> Click to clear the current workspace and start a fresh session.<\/li>\n<li><strong>Search:<\/strong> Search through your previous prompt history and saved chats.<\/li>\n<li><strong>Notes:<\/strong> Access and organize saved workspace notes.<\/li>\n<li><strong>Folders &amp; Chats:<\/strong> Organize your active conversations into dedicated folders or access saved individual chat sessions.<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>2. Main Workspace &amp; Chat Panel<\/summary>\n<div class=\"sub-body\">\n<ul>\n<li><strong>Model Indicator:<\/strong> Displays the active model currently selected for the conversation (e.g., <code>OI llama4:latest<\/code>).<\/li>\n<li><strong>Prompt Input Box:<\/strong>\n<ul>\n<li><strong>Add Files (<code>+<\/code>):<\/strong> Upload documents (PDFs, text files) or images to interact with using Retrieval-Augmented Generation (RAG) or multimodal vision tasks.<\/li>\n<li><strong>Tools\/Plugins (<code>\u2756<\/code>):<\/strong> Access integrations, web tools, or advanced interpreter tools.<\/li>\n<li><strong>Model Switcher Dropdown:<\/strong> Click the dropdown next to the model name inside the prompt box to switch between supported models running on Minerva HPC.<\/li>\n<li><strong>Voice Input (<code>\ud83c\udf99<\/code>):<\/strong> Dictate your prompt using microphone input.<\/li>\n<li><strong>Voice Response Mode (<code>|||<\/code>):<\/strong> Toggle interactive audio mode.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Suggested Starters:<\/strong> Quick-click suggestions to quickly test model capabilities (e.g., study help, brainstorming, or productivity tips).<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>3. Top Control Bar<\/summary>\n<div class=\"sub-body\">\n<ul>\n<li><strong>Controls Icon (<code>\u2699<\/code> \/ Controls):<\/strong> Adjust session-specific model parameters such as temperature, system prompt, or context length.<\/li>\n<li><strong>User Profile &amp; Settings:<\/strong> Access account settings, documentation, keyboard shortcuts, or sign out.<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<\/div>\n<\/details>\n<p><!-- Section 6 --><\/p>\n<details class=\"doc-section\">\n<summary>6. Use Cases &amp; Examples<\/summary>\n<div class=\"section-body\">\n<details class=\"sub-section\">\n<summary>Use Case 1: Asking Scientific Questions<\/summary>\n<div class=\"sub-body\">\n<p>Ask target scientific questions directly via the prompt bar. You can follow up with suggested prompts or type custom questions to explore topics interactively.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Prompt:<\/strong> &#8220;How does CRISPR-Cas9 differ from traditional gene-editing techniques?&#8221;<\/div>\n<p><strong>Example Output Focus:<\/strong> Summarizes aspects like precision\/efficiency, ease of use, specificity, versatility across organisms, and reduced off-target effects compared to TALENs or ZFNs.<\/p>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 2: Summarizing &amp; Chatting with Research Papers (RAG)<\/summary>\n<div class=\"sub-body\">\n<p><strong>Paper Summarization<\/strong> \u2014 Upload documents (e.g., PDFs) by clicking the <code>+<\/code> icon in the input panel and selecting <strong>Upload Files<\/strong>.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Prompt:<\/strong> &#8220;Provide a 10-point summary of the attached research paper.&#8221;<\/div>\n<p><strong>Example Output:<\/strong> Extracts key methodology, framework details (e.g., federated learning across multiple hospitals), evaluation metrics (AUROC values), guidelines followed (TRIPOD), and license info.<\/p>\n<p><strong>Conversational Document Interaction<\/strong> \u2014 Ask follow-up questions specific to the uploaded file&#8217;s contents.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Follow-up Prompt:<\/strong> &#8220;How does the federated model perform compared to the pooled and local models in predicting Acute Kidney Injury (AKI)?&#8221;<\/div>\n<p><strong>Example Output:<\/strong> RAG retrieves contextual details from the paper, explaining that the federated model outperforms local models (especially in smaller datasets) and performs comparably to the pooled model.<\/p>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 3: Explanation of Medical Terms<\/summary>\n<div class=\"sub-body\">\n<p>Translates complex clinical terminology into clear, accessible descriptions.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Prompt:<\/strong> &#8220;What does &#8216;acute kidney injury&#8217; mean?&#8221;<\/div>\n<p><strong>Example Output:<\/strong> Defines AKI as a sudden loss of kidney function over hours or days, provides intuitive analogies (e.g., a coffee filter), lists potential causes (dehydration, NSAIDs\/antibiotics, sepsis) and common symptoms (fatigue, swelling, altered urination patterns).<\/p>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 4: Programming Assistance<\/summary>\n<div class=\"sub-body\">\n<p>Get help writing, debugging, or understanding code in multiple languages.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Prompt:<\/strong> &#8220;I want to perform a t-test in Python. Give me an example code.&#8221;<\/div>\n<div class=\"code-block\">\n<div class=\"code-block-header\"><span class=\"code-lang\">Python<\/span><\/div>\n<pre><code>import numpy as np\nfrom scipy import stats\n\n# Sample data\nnp.random.seed(0)\nsample1 = np.random.normal(0, 1, 100)\nsample2 = np.random.normal(0.5, 1, 100)\n\n# Perform two-sample t-test\nt_stat, p_val = stats.ttest_ind(sample1, sample2)\n\nprint(f\"T-Statistic: {t_stat}\")\nprint(f\"P-Value: {p_val}\")\n\n# Interpret the results\nalpha = 0.05\nif p_val &lt; alpha:\n    print(\"Reject the null hypothesis. The means are likely different.\")\nelse:\n    print(\"Fail to reject the null hypothesis. The means are likely the same.\")<\/code><\/pre>\n<\/div>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 5: Translating Between Languages<\/summary>\n<div class=\"sub-body\">\n<p>Translate clinical notes, queries, or descriptions into practical target languages.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Prompt:<\/strong> &#8220;Translate the following into Spanish: &#8216;How are you feeling today? Checking in on physical symptoms like pain, fatigue, or discomfort can help catch health issues early and guide appropriate care.'&#8221;<\/div>\n<p><strong>Generated Response:<\/strong> &#8220;\u00bfC\u00f3mo te sientes hoy? Revisar los s\u00edntomas f\u00edsicos como dolor, fatiga o molestias puede ayudar a detectar problemas de salud temprano y guiar el cuidado adecuado.&#8221;<\/p>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 6: Comparing Multiple Models Side-by-Side<\/summary>\n<div class=\"sub-body\">\n<p>Evaluate accuracy, response depth, and clarity across different model sizes concurrently.<\/p>\n<p><strong>How to enable:<\/strong> Click the <code>+<\/code> icon next to the active model name in the chat panel to add additional models.<\/p>\n<div class=\"callout callout-accent\"><strong>Example Query:<\/strong> &#8220;What are the main causes of cardiovascular disease?&#8221;<\/div>\n<p><strong>Result:<\/strong> Displays side-by-side columns with each model&#8217;s response generated simultaneously from a single prompt.<\/p>\n<\/div>\n<\/details>\n<details class=\"sub-section\">\n<summary>Use Case 7: API Access &amp; Integration<\/summary>\n<div class=\"sub-body\">\n<p>Integrate the Assistant Tool directly into custom Python scripts, data pipelines, or applications.<\/p>\n<ol>\n<li>Obtain an API key by navigating to <strong>Settings &gt; Account<\/strong> in the web interface.<\/li>\n<li>Use the endpoint URL: <code>https:\/\/assistant.hpc.mssm.edu\/api\/chat\/completions<\/code><\/li>\n<\/ol>\n<div class=\"callout callout-accent\"><strong>Note:<\/strong> Client machines making API calls do not require local GPU resources or Minerva GPU job allocations. All model computation and GPU inference are handled automatically on the central Assistant server backend.<\/div>\n<p><strong>Python API Example:<\/strong><\/p>\n<div class=\"code-block\">\n<div class=\"code-block-header\"><span class=\"code-lang\">Python<\/span><\/div>\n<pre><code>import requests\n\ntoken = \"your_api_token_here\"\nurl = 'https:\/\/assistant.hpc.mssm.edu\/api\/chat\/completions'\n\nheaders = {\n    'Authorization': f'Bearer {token}',\n    'Content-Type': 'application\/json'\n}\n\ndata = {\n    \"model\": \"tinyllama:latest\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": \"What are the main causes of cardiovascular disease?\"\n        }\n    ]\n}\n\nresponse = requests.post(url, headers=headers, json=data)\nresult = response.json()\n\nif 'choices' in result and result['choices']:\n    print(\"Assistant response:\", result['choices'][0]['message']['content'])\nelse:\n    print(\"No assistant response found.\")<\/code><\/pre>\n<\/div>\n<\/div>\n<\/details>\n<\/div>\n<\/details>\n<div class=\"doc-footer\">Minerva HPC Documentation \u2014 AI Assistant Tool<\/div>\n<\/div>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Scientific Computing and Data\u00a0\/\u00a0High Performance Computing \/ Documentation \/ Ollama HPC Documentation Generative AI Assistant Tool on Minerva Overview The generative AI Assistant tool on Minerva (https:\/\/assistant.hpc.mssm.edu\/) is an advanced AI-powered platform designed to support clinicians, researchers, data scientists, and students in their daily work. It enables users to interact with cutting-edge Large Language Models [&hellip;]<\/p>\n","protected":false},"author":624,"featured_media":0,"parent":35,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-8189","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/8189","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/users\/624"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/comments?post=8189"}],"version-history":[{"count":36,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/8189\/revisions"}],"predecessor-version":[{"id":14878,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/8189\/revisions\/14878"}],"up":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/35"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/media?parent=8189"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}