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Transformers.js v4 Preview: Now Available on NPM!
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Sebastian Nagy
Snagy22000
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upvoted an article 13 days ago
Transformers.js v4 Preview: Now Available on NPM! reacted
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MonsterMMORPG's
post with π 16 days ago
Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA & Fine Tuning - Base & Edit Model - https://youtu.be/DPX3eBTuO_Y
This is a full comprehensive step-by-step tutorial for how to train Qwen Image models. This tutorial covers how to do LoRA training and full Fine-Tuning / DreamBooth training on Qwen Image models. It covers both the Qwen Image base model and the Qwen Image Edit Plus 2509 model. This tutorial is the product of 21 days of full R&D, costing over $800 in cloud services to find the best configurations for training. Furthermore, we have developed an amazing, ultra-easy-to-use Gradio app to use the legendary Kohya Musubi Tuner trainer with ease. You will be able to train locally on your Windows computer with GPUs with as little as 6 GB of VRAM for both LoRA and Fine-Tuning. Furthermore, I have shown how to train a character (person), a product (perfume) and a style (GTA5 artworks).
Tutorial Link : https://youtu.be/DPX3eBTuO_Y reacted
to
alvarobartt's
post with π₯ 16 days ago
π₯ `hf-mem` v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the `--experimental` flag!
`uvx hf-mem --model-id ... --experimental` will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.
π‘ Alternatively, you can also set the `--max-model-len`, `--batch-size` and `--kv-cache-dtype` arguments (Γ la vLLM) manually if preferred. Organizations
upvoted an article 13 days ago
reacted to MonsterMMORPG's post with π 16 days ago
Post
2734
Qwen Image Models Training - 0 to Hero Level Tutorial - LoRA & Fine Tuning - Base & Edit Model - https://youtu.be/DPX3eBTuO_Y
This is a full comprehensive step-by-step tutorial for how to train Qwen Image models. This tutorial covers how to do LoRA training and full Fine-Tuning / DreamBooth training on Qwen Image models. It covers both the Qwen Image base model and the Qwen Image Edit Plus 2509 model. This tutorial is the product of 21 days of full R&D, costing over $800 in cloud services to find the best configurations for training. Furthermore, we have developed an amazing, ultra-easy-to-use Gradio app to use the legendary Kohya Musubi Tuner trainer with ease. You will be able to train locally on your Windows computer with GPUs with as little as 6 GB of VRAM for both LoRA and Fine-Tuning. Furthermore, I have shown how to train a character (person), a product (perfume) and a style (GTA5 artworks).
Tutorial Link : https://youtu.be/DPX3eBTuO_Y
This is a full comprehensive step-by-step tutorial for how to train Qwen Image models. This tutorial covers how to do LoRA training and full Fine-Tuning / DreamBooth training on Qwen Image models. It covers both the Qwen Image base model and the Qwen Image Edit Plus 2509 model. This tutorial is the product of 21 days of full R&D, costing over $800 in cloud services to find the best configurations for training. Furthermore, we have developed an amazing, ultra-easy-to-use Gradio app to use the legendary Kohya Musubi Tuner trainer with ease. You will be able to train locally on your Windows computer with GPUs with as little as 6 GB of VRAM for both LoRA and Fine-Tuning. Furthermore, I have shown how to train a character (person), a product (perfume) and a style (GTA5 artworks).
Tutorial Link : https://youtu.be/DPX3eBTuO_Y
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reacted to alvarobartt's post with π₯ 16 days ago
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π₯
π‘ Alternatively, you can also set the
hf-mem v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the --experimental flag!uvx hf-mem --model-id ... --experimental will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.π‘ Alternatively, you can also set the
--max-model-len, --batch-size and --kv-cache-dtype arguments (Γ la vLLM) manually if preferred. 
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reacted to Xenova's post with π₯ about 1 year ago
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First project of 2025: Vision Transformer Explorer
I built a web app to interactively explore the self-attention maps produced by ViTs. This explains what the model is focusing on when making predictions, and provides insights into its inner workings! π€―
Try it out yourself! π
webml-community/attention-visualization
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/attention-visualization
I built a web app to interactively explore the self-attention maps produced by ViTs. This explains what the model is focusing on when making predictions, and provides insights into its inner workings! π€―
Try it out yourself! π
webml-community/attention-visualization
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/attention-visualization
reacted to Xenova's post with π₯ over 1 year ago
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I'm excited to announce that Transformers.js V3 is finally available on NPM! π₯ State-of-the-art Machine Learning for the web, now with WebGPU support! π€―β‘οΈ
Install it from NPM with:
πππ π @πππππππππππ/ππππππππππππ
or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q
Segment Anything demo: webml-community/segment-anything-webgpu
Install it from NPM with:
πππ π @πππππππππππ/ππππππππππππ
or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q
Segment Anything demo: webml-community/segment-anything-webgpu
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