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multimodal language models, seamlessly integrating advanced vision and language understanding within a unified architecture. By leveraging large-scale contrastive pre-training, it establishes a profound connection between image embeddings and textual representations, thereby facilitating precise cross-modal retrieval. This innovative approach has yielded impressive results on benchmark datasets while maintaining an impressively small memory footprint. Moreover, its design incorporates a hierarchical attention mechanism that dynamically focuses on salient visual regions and contextual words, significantly enhancing coherence in generated captions.<\/p>\n<ul style=\"list-style-type:decimal;\">\n<li>Improved performance across various visual-language tasks.<\/li>\n<li>Robust real-time inference capabilities.<\/li>\n<li>Optimized for seamless integration into applications.<\/li>\n<li>Enhanced coherence in generated captions.<\/li>\n<\/ul>\n<table style=\"border-collapse:collapse;\">\n<tr>\n<th><b>Features<\/b><\/th>\n<td>450 million parameters, real-time inference on consumer-grade hardware, diverse image-text pairs for training and curated domain-specific datasets for broad coverage and reduced bias.<\/td>\n<\/tr>\n<\/table>\n<h4>Performance Metrics<\/h4>\n<ul style=\"list-style-type:roman;\">\n<li>Competitive performance across various benchmark datasets.<\/li>\n<li>Faster inference speed on consumer GPUs compared to traditional models.<\/li>\n<li>Broad applicability in visual-language tasks, including image captioning and content moderation.<\/li>\n<\/ul>\n<h4>Design Principles<\/h4>\n<ul style=\"list-style-type:lower-roman;\">\n<li>A hierarchical attention mechanism focusing salient visual regions and contextual words for improved coherence.<\/li>\n<li>A large-scale contrastive pre-training regimen aligning image embeddings with textual representations.<\/li>\n<li>Publicly available image-text pairs and curated domain-specific datasets for broad coverage and reduced bias.<\/li>\n<\/ul>\n<h4>Implementation Considerations<\/h4>\n<ul style=\"list-style-type:decimal;\">\n<li>Real-time inference capabilities suitable for consumer-grade hardware.<\/li>\n<li>Robust performance across diverse visual-language tasks, including image captioning and content moderation.<\/li>\n<li>A hierarchical attention mechanism that dynamically focuses on salient regions and contextual words.<\/li>\n<\/ul>\n<h4>Training Data and Evaluation Metrics<\/h4>\n<ul style=\"list-style-type:lower-roman;\">\n<li>Diverse collection of publicly available image-text pairs for training.<\/li>\n<li>Curated domain-specific datasets to ensure broad coverage and reduced bias.<\/li>\n<li>Competitive performance across benchmark datasets, with real-time inference capabilities on consumer-grade hardware.<\/li>\n<\/ul>\n<h4>Frequently Asked Questions<\/h4>\n<p><q>What is the primary application of the LFM2.5-VL-450M?<\/q><\/p>\n<p>The model is optimized for robust visual-language tasks such as image captioning and content moderation.<\/p>\n<p><q>How does the hierarchical attention mechanism work?<\/q><\/p>\n<p>The hierarchical attention mechanism dynamically focuses on salient visual regions and contextual words, improving coherence in generated captions.<\/p>\n<p><q>What datasets were used for training the model?<\/q><\/p>\n<p>The model was trained on a diverse collection of publicly available image-text pairs, supplemented by curated domain-specific datasets to ensure broad coverage and reduced bias.<\/p>\n<h4>Technical Specifications<\/h4>\n<table style=\"border-collapse:collapse;\">\n<tr>\n<th><b specifications<\/b><\/th>\n<td>450 million parameters, real-time inference on consumer-grade hardware, diverse image-text pairs for training and curated domain-specific datasets for broad coverage and reduced bias.<\/td>\n<\/tr>\n<\/table>\n<h4>Maintenance and Support<\/h4>\n<ul style=\"list-style-type:lower-roman;\">\n<li>Regular software updates to ensure compatibility with changing hardware standards.<\/li>\n<li>Active support for troubleshooting and resolving any technical issues that may arise.<\/li>\n<li>A comprehensive documentation set detailing the model&#8217;s architecture, training procedures, and usage guidelines.<\/li>\n<\/ul>\n<h4>Disclaimer<\/h4>\n<p>The LFM2.5-VL-450M is provided as-is, without any warranties or guarantees. The user assumes all risks associated with the use of this model.<\/p>\n<ol>\n<li>Downloader pulling vision-encoder model layers for local automated drone testing<\/li>\n<li>How to Deploy LFM2.5-VL-450M on AMD\/Nvidia GPU Full Speed NPU Mode No-Code Guide FREE<\/li>\n<li>Downloader pulling custom sentiment mapping checkpoints for offline data intelligence systems<\/li>\n<li>LFM2.5-VL-450M Offline on PC No-Internet Version Direct EXE Setup<\/li>\n<li>Installer pre-configuring modern machine learning dependency matrices on local systems<\/li>\n<li>How to Deploy LFM2.5-VL-450M on Your PC 5-Minute Setup Windows<\/li>\n<li>Setup utility linking custom local LLM pipelines with federated LibreChat workspace grids<\/li>\n<li>How to Autostart LFM2.5-VL-450M 100% Private PC Local Guide FREE<\/li>\n<li>Setup utility enabling DirectML acceleration in WebUI for Intel GPUs<\/li>\n<li>How to Run LFM2.5-VL-450M Locally via Ollama 2<\/li>\n<\/ol>\n<p><a href='https:\/\/anderpup.com\/category\/extensions\/'>https:\/\/anderpup.com\/category\/extensions\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud83d\udee1\ufe0f Checksum: bc175622d5e901e5f206cfb5d87637b7 \u2014 \u23f0 Updated on: 2026-07-19 Verify Processor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 \/ RTX 4090 recommended for 26B-A4B fast inference Awareness of Complexities<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62],"tags":[],"class_list":["post-3061","post","type-post","status-publish","format-standard","hentry","category-ollama"],"_links":{"self":[{"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/posts\/3061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/comments?post=3061"}],"version-history":[{"count":1,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/posts\/3061\/revisions"}],"predecessor-version":[{"id":3062,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/posts\/3061\/revisions\/3062"}],"wp:attachment":[{"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/media?parent=3061"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/categories?post=3061"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lmlegal.nl\/index.php\/wp-json\/wp\/v2\/tags?post=3061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}