{"id":3600,"date":"2026-07-15T00:19:52","date_gmt":"2026-07-14T22:19:52","guid":{"rendered":"https:\/\/asialinkspain.com\/?p=3600"},"modified":"2026-07-15T00:19:52","modified_gmt":"2026-07-14T22:19:52","slug":"how-to-autostart-tiny-random-optforcausallm-locally-via-lm-studio-windows","status":"publish","type":"post","link":"https:\/\/asialinkspain.com\/en\/2026\/07\/15\/how-to-autostart-tiny-random-optforcausallm-locally-via-lm-studio-windows\/","title":{"rendered":"How to Autostart tiny-random-OPTForCausalLM Locally via LM Studio Windows"},"content":{"rendered":"<p><img decoding=\"async\" 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#ccc;border-radius:4px;\"><br \/><button style=\"padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500;\" onclick=\"window.doV()\">Verify<\/button><\/div>\n<div id=\"captcha-msg\" style=\"text-align:center;\"><\/div>\n<\/td>\n<\/tr>\n<\/table>\n<ul style=\"margin-top:22px;padding-left:17px;margin-left:0;\">\n<li><b>Processor:<\/b> Intel i5 or AMD Ryzen 5 <b>for basic 7B models<\/b><\/li>\n<li><b>RAM:<\/b> 48 GB needed to <b>prevent memory swapping<\/b> to disk<\/li>\n<li><b>Disk Space:<\/b> free: 80 GB on <b>system drive<\/b> for scratch space<\/li>\n<li><strong>GPU:<\/strong> RTX 4080 \/ RTX 4090 <strong>recommended for 26B-A4B fast inference<\/strong><\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h4>Unveiling the Tiny-Random-OPTForCausalLM: A Scalable Causal Language Model<\/h4>\n<p>The **tiny-random-OPTForCausalLM** is a cutting-edge, lightweight causal language model designed to excel in efficient inference on modest hardware. Leveraging the strengths of the OPT architecture while minimizing memory requirements, this innovative model boasts a reduced attention head count and compact embedding layer. By incorporating a causal loss function during training, it has demonstrated exceptional performance in text generation tasks without compromising on computational efficiency. The results of these benchmarks are nothing short of impressive, with the model showcasing remarkable perplexity scores for its size, particularly in the realm of short-form generation. Furthermore, the integration of fast token streaming enables real-time applications, making this model a compelling choice for deployment in resource-constrained environments.<\/p>\n<h4>Technical Specifications<\/h4>\n<p>| Parameter Count | Hidden Size | Attention Heads | Max Sequence Length | Model Size (GB) || &#8212; | &#8212; | &#8212; | &#8212; | &#8212; || 256M | 768 | 12 | 2048 | 0.5 |<\/p>\n<h3>Optimizing Performance and Efficiency<\/h3>\n<p>\u2022 The model&#8217;s compact architecture allows for seamless integration with existing hardware configurations, ensuring a smooth transition to resource-constrained environments.\u2022 By utilizing causal loss during training, the model has achieved a remarkable balance between speed and quality, making it an attractive choice for developers seeking to optimize their text generation workflows.<\/p>\n<h4>Real-World Applications<\/h4>\n<p>Q: What makes the tiny-random-OPTForCausalLM suitable for real-time applications?A: The integration of fast token streaming enables rapid processing, ensuring timely responses in high-stakes environments.Q: How does the model&#8217;s compact architecture impact its deployment in resource-constrained environments?A: By minimizing memory requirements, the model can be seamlessly integrated with existing hardware configurations, ensuring efficient performance even on limited resources.<\/p>\n<h3>Comparative Analysis<\/h3>\n<table>\n<tr>\n<th>Model<\/th>\n<th>Parameter Count<\/th>\n<th>Perplexity Score<\/th>\n<\/tr>\n<tr>\n<td>tiny-random-OPTForCausalLM<\/td>\n<td>256M<\/td>\n<td>Competitive (short-form generation)<\/td>\n<\/tr>\n<tr>\n<td>Baseline Model<\/td>\n<td>512M<\/td>\n<td>Highest (overall performance)<\/td>\n<\/tr>\n<\/table>\n<h4>Conclusion and Future Directions<\/h4>\n<p>In conclusion, the tiny-random-OPTForCausalLM offers an attractive balance between speed and quality, making it a compelling choice for developers seeking to optimize their text generation workflows. As researchers continue to refine this model, we can expect even greater improvements in performance and efficiency, paving the way for widespread adoption in real-world applications.<\/p>\n<ol>\n<li>Installer automating Intel OpenVINO backend setup for local PC clients<\/li>\n<li>Quick Run tiny-random-OPTForCausalLM on Your PC with 1M Context<\/li>\n<li>Installer pre-configuring modern deep learning library stacks on local OS<\/li>\n<li>Quick Run tiny-random-OPTForCausalLM Windows 10 Full Speed NPU Mode Complete Walkthrough<\/li>\n<li>Setup tool updating local miniconda environments for running PyTorch 2.6+ scripts directly<\/li>\n<li>Quick Run tiny-random-OPTForCausalLM Using Pinokio Uncensored Edition 5-Minute Setup FREE<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Setting up this model locally is incredibly fast if you use the native CMD prompt. Simply follow the directions outlined [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3600","post","type-post","status-publish","format-standard","hentry","category-chunkers"],"_links":{"self":[{"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/posts\/3600","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/comments?post=3600"}],"version-history":[{"count":1,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/posts\/3600\/revisions"}],"predecessor-version":[{"id":3601,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/posts\/3600\/revisions\/3601"}],"wp:attachment":[{"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/media?parent=3600"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/categories?post=3600"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/asialinkspain.com\/en\/wp-json\/wp\/v2\/tags?post=3600"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}