{"id":4338,"date":"2024-05-08T01:46:24","date_gmt":"2024-05-07T16:46:24","guid":{"rendered":"https:\/\/www.wavesource.co.kr\/wavesource\/?p=4338"},"modified":"2024-06-07T19:30:35","modified_gmt":"2024-06-07T10:30:35","slug":"llama-34b-model-multimodal-large-language-model-rtx-3090fe","status":"publish","type":"post","link":"https:\/\/www.wavesource.co.kr\/wavesource\/?p=4338","title":{"rendered":"LLaVa 34B model (multimodal large language model) RTX 3090FE"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"LLaVa 34B model (multimodal large language model) RTX 3090FE 24GB\" width=\"1170\" height=\"658\" src=\"https:\/\/www.youtube.com\/embed\/_6_CGXLB2s0?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\"><br>In a comprehensive evaluation, we tested the LLaVa 34B model, a multimodal large language model celebrated for its ability to process and interpret both text and image data. The focus of this study was to assess the model&#8217;s proficiency in image recognition while operating in English, juxtaposed with its performance in Korean. Our methodology involved exposing the model to a variety of image datasets, requiring it to identify and describe the visual content. While the results showcased a high degree of accuracy and descriptive power in image recognition when operating in English, it became evident that the model&#8217;s performance was markedly less adept when processing tasks in Korean. This disparity highlights the challenges faced by large language models in maintaining consistent performance across different languages, particularly in multimodal tasks. These findings have significant implications for the development of language-specific training approaches and the optimization of multimodal models for diverse linguistic backgrounds. <br><br>\ud14d\uc2a4\ud2b8\uc640 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub97c \ubaa8\ub450 \ucc98\ub9ac\ud558\uace0 \ud574\uc11d\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc73c\ub85c \uc720\uba85\ud55c \uba40\ud2f0 \ubaa8\ub2ec \ub300\ud615 \uc5b8\uc5b4 \ubaa8\ub378\uc778 LLaVa 34B \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8\ud588\uc2b5\ub2c8\ub2e4. \uc774 \ud14c\uc2a4\ud2b8 \ucd08\uc810\uc740 \ubaa8\ub378\uc774 \uc601\uc5b4\ub85c \uc791\ub3d9\ud560 \ub54c\uc758 \uc774\ubbf8\uc9c0 \uc778\uc2dd \ub2a5\ub825\uacfc \ud55c\uad6d\uc5b4\ub85c\uc758 \uc131\ub2a5\uc744 \ube44\uad50\ud558\ub294 \uac83\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\uc758 \ubc29\ubc95\ub860\uc740 \ubaa8\ub378\uc5d0 \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub178\ucd9c\uc2dc\ud0a4\uace0 \uc2dc\uac01\uc801 \ub0b4\uc6a9\uc744 \uc2dd\ubcc4\ud558\uace0 \ubb18\uc0ac\ud558\ub3c4\ub85d \uc694\uad6c\ud558\ub294 \uac83\uc744 \ud3ec\ud568\ud588\uc2b5\ub2c8\ub2e4. \uc601\uc5b4\ub85c \uc791\ub3d9\ud560 \ub54c \uc774\ubbf8\uc9c0 \uc778\uc2dd\uc5d0\uc11c \ub192\uc740 \uc815\ud655\ub3c4\uc640 \ubb18\uc0ac\ub825\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc9c0\ub9cc, \ud55c\uad6d\uc5b4\ub85c \ucc98\ub9ac\ud558\ub294 \uc791\uc5c5\uc5d0\uc11c\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \ud604\uc800\ud788 \ub5a8\uc5b4\uc9c0\ub294 \uac83\uc73c\ub85c \ub098\ud0c0\ub0ac\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ucc28\uc774\ub294 \uba40\ud2f0\ubaa8\ub2ec \uc791\uc5c5\uc5d0\uc11c \ub2e4\uc591\ud55c \uc5b8\uc5b4\uc5d0 \uac78\uccd0 \uc77c\uad00\ub41c \uc131\ub2a5\uc744 \uc720\uc9c0\ud558\ub294 \ub370 \ub300\ud55c \ub300\ud615 \uc5b8\uc5b4 \ubaa8\ub378\uc774 \uc9c1\uba74\ud558\ub294 \ub3c4\uc804\uc744 \uac15\uc870\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc1c\uacac\uc740 \uc5b8\uc5b4\ubcc4 \ud6c8\ub828 \uc811\uadfc \ubc29\uc2dd\uc758 \uac1c\ubc1c\uacfc \ub2e4\uc591\ud55c \uc5b8\uc5b4 \ubc30\uacbd\uc5d0 \ub300\ud55c \uba40\ud2f0\ubaa8\ub2ec \ubaa8\ub378\uc758 \ucd5c\uc801\ud654\uc5d0 \uc911\uc694\ud55c \ud568\uc758\ub97c \uac00\uc9d1\ub2c8\ub2e4<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":4340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[138,164,42,88,46,43,152,166,155,151,140,165,82],"class_list":["post-4338","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wavesource","tag-3090fe","tag-34b","tag-ai","tag-chat","tag-chatgpt","tag-gpt","tag-llama","tag-llava","tag-llm","tag-meta","tag-model","tag-multimodal","tag-nvidia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - 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