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Chukwuma Nwaugha

nwaughachukwuma
vlm-run
5 1 16
projectlosangeles's profile picture asigalov61's profile picture sudanenator's profile picture
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https://nwaughachukwuma.github.io/portfolio

AI & ML interests

NLP for Sentiments and Emotions Analysis; Word Similarities; Text relevance within the context of a Sentence; Word search

Recent Activity

repliedto their post 6 days ago
Can a text-only model + a vision toolkit (mm-ctx) match a native vision model? We benchmarked 4 setups on 23 multimodal tasks (image, video, audio, PDF): • glm-5.2 (text-only) + mm-ctx: 88.4 • gemini-3.5-flash (vision): 83 • deepseek-v4-pro (text-only) + mm-ctx: 79.4 • qwen3.6-35b-a3b (vision): 44.3 The best text-only setup `glm-5.2 + mm` outperformed gemini-3.5-flash, the top vision model, by 5.4 points (6.5%). It was also: • 1.5x faster (100s vs 150s mean per task) • the only setup with zero timeouts (46/46 completed; gemini timed out 4x on bulk-image and long-video tasks) • the only setup stable across runs (88.5 / 88.4) • top on video (100.0), image (91.7), and PDF (90.0) tasks The trade-offs: the toolkit consumed 3.3x more tokens (4.25M vs 1.28M), and lost on audio (85.6 vs 71.3). On completed tasks alone the two are nearly identical (91.0 vs 88.4): the toolkit's edge is efficient extraction that keeps long media tasks inside the time budget. Full report: https://huggingface.co/blog/vlm-run/text-only-models-with-mm
repliedto their post 7 days ago
Can a text-only model + a vision toolkit (mm-ctx) match a native vision model? We benchmarked 4 setups on 23 multimodal tasks (image, video, audio, PDF): • glm-5.2 (text-only) + mm-ctx: 88.4 • gemini-3.5-flash (vision): 83 • deepseek-v4-pro (text-only) + mm-ctx: 79.4 • qwen3.6-35b-a3b (vision): 44.3 The best text-only setup `glm-5.2 + mm` outperformed gemini-3.5-flash, the top vision model, by 5.4 points (6.5%). It was also: • 1.5x faster (100s vs 150s mean per task) • the only setup with zero timeouts (46/46 completed; gemini timed out 4x on bulk-image and long-video tasks) • the only setup stable across runs (88.5 / 88.4) • top on video (100.0), image (91.7), and PDF (90.0) tasks The trade-offs: the toolkit consumed 3.3x more tokens (4.25M vs 1.28M), and lost on audio (85.6 vs 71.3). On completed tasks alone the two are nearly identical (91.0 vs 88.4): the toolkit's edge is efficient extraction that keeps long media tasks inside the time budget. Full report: https://huggingface.co/blog/vlm-run/text-only-models-with-mm
repliedto their post 7 days ago
Can a text-only model + a vision toolkit (mm-ctx) match a native vision model? We benchmarked 4 setups on 23 multimodal tasks (image, video, audio, PDF): • glm-5.2 (text-only) + mm-ctx: 88.4 • gemini-3.5-flash (vision): 83 • deepseek-v4-pro (text-only) + mm-ctx: 79.4 • qwen3.6-35b-a3b (vision): 44.3 The best text-only setup `glm-5.2 + mm` outperformed gemini-3.5-flash, the top vision model, by 5.4 points (6.5%). It was also: • 1.5x faster (100s vs 150s mean per task) • the only setup with zero timeouts (46/46 completed; gemini timed out 4x on bulk-image and long-video tasks) • the only setup stable across runs (88.5 / 88.4) • top on video (100.0), image (91.7), and PDF (90.0) tasks The trade-offs: the toolkit consumed 3.3x more tokens (4.25M vs 1.28M), and lost on audio (85.6 vs 71.3). On completed tasks alone the two are nearly identical (91.0 vs 88.4): the toolkit's edge is efficient extraction that keeps long media tasks inside the time budget. Full report: https://huggingface.co/blog/vlm-run/text-only-models-with-mm
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