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Kasnol
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liked a dataset 28 days ago
alvanlii/cantonese-radio
reacted to Kseniase's post with 🔥 28 days ago
8 New Applications of Test-Time Scaling We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance. Here are 8 fresh studies on test-time scaling: 1. https://huggingface.co/papers/2502.05171 Introduces an LM that scales TTC by reasoning in latent space instead of generating more tokens with no special training. Here, a recurrent block to processes information iteratively. 2. https://huggingface.co/papers/2502.04728 Shows how TTS is applied to enhance model's Planning Domain Definition Language (PDDL) reasoning capabilities, which can be used to generate a symbolic world model. 3. https://huggingface.co/papers/2502.06703 Analyzes optimal TTS strategies and shows how small models can outperform much larger ones. 4. https://huggingface.co/papers/2502.04128 Shows how TTS improves expressiveness, timbre consistency and accuracy in speech synthesis with Llasa framework. It also dives into benefits of scaling train-time compute. 5. https://huggingface.co/papers/2502.07154 Suggests a modified training loss for better reasoning of LLMs when scaling TTC. 6. https://huggingface.co/papers/2502.05078 Unifies the strengths of chain, tree, and graph paradigms into one framework that expands reasoning only on necessary subproblems. 7. https://huggingface.co/papers/2502.01839 Explores scaling trends of self-verification and how to improve its capabilities with TTC. 8. https://huggingface.co/papers/2501.14723 Explores how scaling serial compute (iterations) and parallel compute (trajectories), can improve accuracy in real-world software engineering issues. Also, explore our article about TTS for more -> https://huggingface.co/blog/Kseniase/testtimecompute
reacted to prithivMLmods's post with 👍 4 months ago
GRID-6X : Layout for Seamless Image Assembly 🔥 🪨Demo: https://huggingface.co/spaces/prithivMLmods/GRID-6X 🪨Doc / Blog: https://huggingface.co/blog/prithivMLmods/grid-6x In the `infer` function: ```python grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y)) for i, img in enumerate(result.images[:num_images]): grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height)) ``` 1. **Image Initialization**: `grid_img` is a blank canvas that will hold the images in a grid format. 2. **Image Placement**: Images are pasted onto the canvas using a loop: - **Horizontal Position**: `(i % grid_size_x) * width` calculates the x-coordinate. - **Vertical Position**: `(i // grid_size_x) * height` calculates the y-coordinate. 1. **Grid Size Selection**: The user selects the grid size from options like "2x1", "1x2", "2x2", "2x3", "3x2", and "1x1". Each option corresponds to the arrangement of images: - **2x1**: 2 images in a single row - **1x2**: 1 image in two rows (column layout) - **2x2**: 2 rows with 2 images each - **2x3**: 2 rows with 3 images each - **3x2**: 3 rows with 2 images each - **1x1**: A single image (default) 2. **Image Generation**: Based on the grid size selection, the app calculates the number of images to generate. For example: - If the grid size is "2x2", the app generates 4 images. - For "3x2", it generates 6 images. -> Each option arranges images accordingly, providing flexibility in viewing multiple images in one output. -> Added both of these spaces that support the GRID functionality Layout for Seamless Image Assembly : ---------- 🔥IMAGINEO-4K: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K 🔥GRID-6X: https://huggingface.co/spaces/prithivMLmods/GRID-6X ---------- . . .@prithivMLmods 🤗
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