Upload folder using huggingface_hub

0.codes.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d7415feae8b3ddc4e90d8686f5bb357f048c082cdcd2dd9e1433a20018652a32
-size 2290908
+oid sha256:115e5376ea063e483644fa6a84ad65a0061bd11baaafbd38f507ab0493f4eb57
+size 2294556

0.metadata.json

@@ -1,6 +1,6 @@
 {
     "passage_offset": 0,
-    "num_passages": 3328,
-    "num_embeddings": 572442,
+    "num_passages": 3334,
+    "num_embeddings": 573356,
     "embedding_offset": 0
 }

0.residuals.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:aa7c9d3a86cf0560bfa607c04924e2ff3025ac668718111dcbf0eae2d2548568
-size 73273776
+oid sha256:db2f8f475b84a3c06061c69037f84a002b14208b1f1b0290f270c8457e7dd4bc
+size 73390768

avg_residual.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a17a2d1a21bf43df522cfbbd82dd39fcb2adca9b4cf1b561b9ff1f856df5a11
+oid sha256:0ebc69ec469a0e1b009352f0b213fcf115ad518e037ebb772271f49f310938e3
 size 1205

buckets.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eb15485cca91f270cde97f20eab75335bbc4e6df44b4a4180a1cd39517711285
+oid sha256:6621d4cce97c36ac309d397373eda4e3fa055a5803f7b549357bda1441b52bc0
 size 2904

centroids.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:daa598b9538edace39d8c618092e997cd8d54cea5b445b5647b9f65bf049c0e6
+oid sha256:c3c7a863a444a611068db6b6fd58c96379dbe1e77f090103c04ef88f9796fab0
 size 2098342

collection.json

@@ -3326,5 +3326,11 @@
   "Within a time budget that regular decoding does not finish, our system is able to generate sequences with HumanEval Pass@First of 43% and Pass@All of 61%, far exceeding what's feasible with single-sequence speculative decoding. Our peak GPU utilization during decoding reaches as high as 15.8%, more than 3X the highest of that of regular decoding and around 10X of single-sequence speculative decoding.",
   "The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (<35\\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode.",
   "Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7times acceleration in training speed compared to contrastive learning on web-scale data. Through extensive experiments spanning diverse vision tasks, including detection and segmentation, we demonstrate that the proposed method maintains high representation quality. Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet.",
-  "Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios."
+  "Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios.",
+  "3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability, or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process, restricting the scope for direct and versatile 3D modifications. In this work, we introduce Interactive3D, an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages, utilizing distinct 3D representations.",
+  "In this work, we introduce Interactive3D, an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages, utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction, allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components, (ii) Deformable and Rigid Dragging, (iii) Geometric Transformations, and (iv) Semantic Editing. Subsequently, the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/.",
+  "Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real world, which are characterized by the presence of extensive texts embedded within images. Recently, the advent of MLLMs with impressive versatility has raised the bar for what we can expect from MLLMs. However, their proficiency in text-rich scenarios has yet to be comprehensively and objectively assessed, since current MLLM benchmarks primarily focus on evaluating general visual comprehension. In this work, we introduce SEED-Bench-2-Plus, a benchmark specifically designed for evaluating text-rich visual comprehension of MLLMs. Our benchmark comprises 2.3K multiple-choice questions with precise human annotations, spanning three broad categories: Charts, Maps, and Webs, each of which covers a wide spectrum of text-rich scenarios in the real world. These categories, due to their inherent complexity and diversity, effectively simulate real-world text-rich environments. We further conduct a thorough evaluation involving 34 prominent MLLMs (including GPT-4V, Gemini-Pro-Vision and Claude-3-Opus) and emphasize the current limitations of MLLMs in text-rich visual comprehension.",
+  "We further conduct a thorough evaluation involving 34 prominent MLLMs (including GPT-4V, Gemini-Pro-Vision and Claude-3-Opus) and emphasize the current limitations of MLLMs in text-rich visual comprehension. We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs. The dataset and evaluation code can be accessed at https://github.com/AILab-CVC/SEED-Bench.",
+  "In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model -- InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448times448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.",
+  "We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks. Code has been released at https://github.com/OpenGVLab/InternVL."
 ]

doclens.0.json

@@ -1 +1 @@
-[178,205,218,148,184,163,221,185,200,228,172,155,210,222,88,206,226,67,132,212,91,206,104,212,174,205,132,159,230,175,216,198,227,190,212,198,122,213,169,204,92,197,118,191,191,224,69,219,197,72,218,77,175,111,155,217,220,170,231,91,221,217,95,146,177,123,195,205,151,209,207,36,202,200,226,176,232,53,167,199,89,184,213,104,154,153,216,214,215,174,205,72,211,78,221,212,232,223,73,158,220,158,202,222,189,165,205,175,222,132,126,179,219,110,209,158,208,98,176,192,226,34,158,205,126,178,224,182,227,100,152,191,169,195,163,172,208,117,199,217,167,217,157,163,194,217,200,217,23,221,209,146,150,204,200,125,215,232,68,147,212,41,223,178,152,173,210,139,198,182,196,207,95,176,205,223,83,216,85,207,210,52,177,178,230,197,119,226,99,182,210,212,77,138,199,123,179,111,219,69,223,65,204,215,83,197,87,211,132,216,135,178,157,166,216,85,170,195,208,190,175,134,220,200,67,221,211,66,227,222,226,190,209,205,67,207,139,208,127,186,205,168,221,179,223,117,148,221,216,80,189,125,199,202,64,218,77,195,190,221,181,98,143,214,220,97,187,127,219,122,216,87,138,212,194,112,219,227,101,220,100,164,234,109,221,102,223,89,184,205,219,77,188,223,172,171,175,152,175,137,213,197,114,205,221,138,181,174,227,73,147,144,178,147,215,152,182,204,80,210,123,211,121,209,224,224,219,211,163,133,187,148,151,163,221,94,133,213,72,187,224,216,162,154,224,184,118,204,220,154,117,220,162,202,223,195,110,197,151,224,88,182,217,221,214,118,218,118,164,205,97,221,183,154,206,197,74,170,219,103,230,215,192,224,78,184,72,201,227,221,191,181,104,190,224,221,99,123,206,102,228,202,74,195,96,225,176,232,231,96,225,206,61,173,101,190,211,90,213,199,203,184,209,173,160,207,203,99,201,195,132,195,214,148,211,227,192,215,212,127,162,213,114,178,111,207,71,129,182,212,75,176,209,137,213,224,220,70,232,116,228,179,200,184,222,93,202,71,219,123,213,119,204,173,135,118,207,96,216,107,210,202,189,172,211,76,146,195,169,83,227,62,219,223,125,158,202,226,98,214,225,76,156,211,204,110,190,224,200,64,228,223,72,213,102,214,178,219,128,211,127,187,213,160,216,134,202,150,186,201,195,230,199,206,193,205,219,216,84,213,223,192,222,109,187,209,79,153,204,224,72,167,190,183,183,228,133,227,103,197,225,203,205,201,207,213,210,78,138,195,133,195,180,190,185,207,189,100,191,229,198,129,226,135,205,164,226,190,73,200,231,209,216,147,221,228,165,213,209,181,157,222,117,216,58,148,219,92,222,219,63,191,218,186,197,158,172,204,109,204,102,223,229,91,192,217,186,204,144,173,209,95,186,222,225,178,183,222,71,193,182,232,63,227,228,82,209,162,219,183,222,122,212,195,60,219,130,223,69,216,111,198,224,80,223,74,164,148,227,193,216,139,207,198,148,227,199,169,230,225,86,138,165,206,91,213,164,215,229,173,216,210,92,166,205,102,196,78,202,74,187,202,229,191,222,57,206,115,183,94,225,173,198,109,212,224,126,215,44,221,80,196,95,177,218,217,198,109,218,191,221,214,227,71,207,123,206,90,219,218,108,181,214,123,150,204,214,208,225,199,191,192,213,93,217,149,178,199,106,204,80,208,60,131,132,232,218,213,115,202,94,175,227,217,114,200,222,178,180,185,192,193,205,173,226,217,222,197,184,205,211,136,214,221,62,213,89,169,184,135,198,98,216,225,71,230,133,213,154,216,189,122,224,108,204,88,207,227,93,191,204,214,66,153,219,209,203,218,237,229,222,148,174,218,107,214,169,223,216,173,220,148,199,188,175,203,142,188,210,98,167,174,224,103,156,97,220,111,216,70,130,221,216,115,166,164,162,210,216,69,173,203,77,216,175,172,210,98,199,213,88,221,82,184,131,201,220,133,211,116,156,219,213,227,128,221,237,172,217,64,180,221,70,169,220,216,134,215,177,190,197,229,221,226,74,189,190,219,86,222,74,213,106,184,182,205,221,229,143,201,198,162,209,128,221,209,60,216,146,228,106,215,154,214,202,82,194,141,168,217,213,80,140,114,220,149,229,215,150,211,170,199,109,218,108,206,125,204,164,183,148,193,153,188,143,160,199,210,124,225,105,178,75,222,112,205,75,199,83,222,215,158,104,205,70,215,223,158,220,99,124,194,91,219,65,209,149,234,188,222,218,200,66,208,101,208,188,201,207,198,160,205,219,118,170,195,141,128,182,189,211,51,179,216,112,152,196,224,80,223,177,218,127,186,113,151,117,159,184,200,231,212,134,222,68,216,204,90,151,202,158,173,146,144,209,222,89,171,188,206,215,117,191,223,80,215,68,151,159,210,74,208,105,204,105,174,228,67,147,202,214,41,222,163,183,142,227,188,200,208,132,157,191,195,94,166,201,155,214,132,185,195,165,214,219,156,217,100,147,220,105,220,223,225,117,176,166,207,134,218,202,172,183,217,76,229,139,217,87,208,181,214,169,154,199,231,208,200,157,213,222,49,207,191,139,219,218,82,122,154,137,190,176,192,180,231,80,208,136,185,112,189,187,192,208,219,110,208,102,186,225,220,218,235,214,96,133,198,199,95,152,83,219,190,124,148,222,213,207,145,217,168,200,32,208,198,171,205,218,104,218,136,230,77,139,178,212,214,129,211,233,93,221,95,155,153,221,151,201,108,193,216,172,208,72,177,217,208,225,60,81,172,234,180,146,184,180,199,222,224,182,106,183,105,221,208,81,221,137,211,44,198,224,205,82,187,164,147,213,163,229,192,219,218,190,228,221,218,116,231,97,182,126,213,193,173,207,103,207,122,161,156,193,153,192,92,198,155,223,224,106,223,170,177,225,40,206,203,221,152,208,116,203,105,197,221,65,226,111,224,215,215,142,154,211,84,184,135,169,212,213,76,229,225,130,207,85,209,177,116,140,150,229,209,105,164,186,214,219,62,148,203,230,118,207,216,217,211,114,201,134,217,87,197,124,209,186,184,164,189,163,212,85,219,106,209,220,168,221,212,112,189,183,229,200,192,106,224,114,220,95,141,223,222,219,226,79,187,202,212,222,193,128,223,209,160,178,197,222,111,152,114,164,206,179,189,183,122,212,149,150,130,214,115,195,118,204,208,120,195,163,159,220,125,222,174,211,144,140,220,80,173,204,87,215,149,227,97,229,221,119,213,139,212,82,215,68,213,136,186,168,202,85,213,93,168,218,63,222,121,213,90,218,94,197,221,182,220,112,224,221,83,215,194,138,181,197,221,91,186,194,99,179,213,210,76,188,214,216,79,213,92,220,94,201,108,213,131,221,153,219,74,159,172,218,217,219,108,209,35,217,112,157,206,148,211,126,214,107,191,105,164,224,220,126,204,201,211,119,203,129,165,205,130,218,137,211,178,204,215,206,119,209,75,193,182,115,178,207,217,220,184,199,167,189,230,140,208,167,173,195,136,220,205,64,186,159,194,75,213,189,110,189,208,120,176,182,162,196,183,137,134,198,172,193,184,172,212,189,214,200,95,167,199,110,195,116,180,204,66,217,126,222,226,193,166,143,121,218,224,196,140,226,142,206,66,214,140,218,144,201,221,152,204,154,221,101,225,69,216,212,128,181,200,180,206,136,193,226,226,219,141,168,202,209,105,178,128,208,197,145,163,218,124,220,147,220,156,211,69,212,226,132,223,165,211,110,229,194,193,204,208,90,218,106,152,215,134,180,182,204,211,80,211,108,204,102,208,102,192,94,191,206,203,140,165,213,197,105,186,186,210,62,208,165,213,174,186,115,211,164,193,128,201,169,217,167,217,174,222,167,129,170,217,218,182,159,113,208,85,219,126,219,89,172,213,212,69,205,135,124,205,67,208,52,186,160,225,125,190,65,227,168,180,166,151,194,205,88,138,204,110,124,223,80,193,106,179,183,163,220,148,205,218,200,221,198,202,190,213,87,227,239,81,213,115,211,219,201,204,105,216,83,210,95,170,219,197,86,228,86,209,125,231,83,198,194,171,144,183,222,216,216,226,204,109,216,115,180,227,116,207,223,84,190,215,117,212,79,214,230,218,134,208,92,209,99,181,220,61,212,85,199,185,174,171,220,221,57,146,224,214,81,183,218,152,163,193,72,203,111,130,222,204,217,229,209,201,189,220,213,72,192,214,221,157,197,124,209,100,223,162,223,73,85,221,68,210,46,205,76,118,160,180,200,85,218,34,196,170,204,88,168,224,71,188,207,166,181,210,101,148,215,76,219,217,104,199,180,210,208,69,201,202,216,224,89,206,88,128,125,196,203,106,183,182,84,228,200,120,171,166,153,215,210,82,210,65,163,131,213,83,180,205,92,152,228,99,212,212,209,94,215,218,95,206,179,236,219,204,102,196,110,160,218,140,228,102,218,177,172,193,95,209,215,203,220,78,213,191,189,168,204,218,192,89,213,223,208,230,168,202,169,222,56,155,203,73,197,156,208,222,127,212,223,160,217,201,223,205,217,100,221,208,143,218,22,208,224,86,209,90,220,144,182,139,219,79,194,183,225,216,209,190,102,221,220,203,152,215,113,230,106,212,221,106,197,111,185,211,83,162,200,208,219,124,210,92,161,216,221,79,208,166,211,142,179,222,197,79,185,226,182,193,195,170,202,113,192,231,228,197,180,152,223,155,215,97,213,85,205,145,200,89,209,201,139,217,163,133,206,174,112,205,213,217,123,201,136,132,221,206,105,200,139,183,160,215,134,221,208,181,208,101,210,225,116,183,217,80,186,120,162,194,201,97,188,209,174,122,229,214,205,120,218,228,228,83,134,201,119,171,210,55,227,107,225,64,174,203,130,213,226,95,206,157,139,154,171,139,224,219,154,190,206,218,217,221,86,135,217,213,183,118,187,186,128,212,79,184,186,156,188,134,216,172,155,195,210,176,223,125,128,217,213,214,217,94,189,192,133,167,85,187,217,60,158,221,122,217,117,224,95,206,89,199,74,205,210,70,209,83,196,77,169,188,228,114,188,105,229,182,174,223,59,181,205,127,225,222,128,190,219,112,197,115,222,205,115,184,214,210,238,196,212,85,207,86,183,215,178,187,112,194,176,212,90,217,80,164,179,187,138,221,214,203,96,227,198,82,209,207,96,208,76,228,224,205,207,186,178,221,224,189,143,229,182,229,132,213,201,186,218,67,222,221,83,216,44,220,68,214,103,137,210,48,210,211,88,222,169,225,155,181,200,207,62,219,152,179,130,215,86,183,90,202,164,179,205,128,219,224,148,190,117,213,221,184,175,146,223,216,82,230,92,201,231,218,172,207,209,189,150,216,142,226,46,197,219,92,228,168,216,111,221,204,216,190,180,189,206,108,204,101,224,74,223,65,181,142,211,203,122,220,214,66,179,185,222,116,120,209,222,101,172,209,232,56,223,177,212,107,191,168,193,193,105,212,105,163,209,191,117,194,170,211,190,121,221,90,161,207,67,226,90,216,60,171,219,89,220,184,214,194,144,182,214,208,196,133,226,214,77,190,215,63,115,208,92,227,204,68,197,77,178,188,204,216,202,194,152,175,181,208,131,196,223,128,177,116,207,102,220,121,216,199,202,207,74,211,196,217,177,214,218,221,93,219,192,215,143,139,183,226,83,102,219,211,100,194,47,119,204,160,143,180,149,183,226,96,172,212,186,198,207,182,207,221,164,146,224,61,200,140,147,196,214,60,226,215,111,216,186,180,161,234,196,124,208,214,221,189,178,112,168,208,136,169,181,202,124,201,74,211,143,192,175,119,163,202,67,191,172,148,179,223,122,217,117,180,190,186,209,72,193,182,218,80,156,138,201,202,218,208,149,190,109,224,64,192,114,219,199,210,208,97,232,205,197,188,189,87,213,152,196,164,131,225,94,219,205,163,191,172,196,189,206,110,201,73,191,122,210,173,208,73,221,136,222,185,224,74,213,86,185,221,170,210,69,165,156,210,102,211,210,221,197,209,154,127,212,180,208,139,231,207,50,166,96,150,158,206,228,214,202,95,162,220,191,136,217,54,155,201,140,179,191,91,213,220,74,145,216,232,45,208,217,209,182,160,182,100,221,155,219,227,160,180,209,147,174,212,67,209,82,213,148,208,51,176,88,210,71,117,174,218,82,211,188,170,210,186,136,176,220,157,189,167,190,212,160,212,135,201,219,49,162,165,209,117,175,213,152,176,220,221,124,150,204,220,73,228,194,218,57,195,173,159,173,175,206,176,229,79,164,201,203,152,214,116,137,219,66,222,214,80,175,202,90,225,113,219,206,78,190,89,214,50,209,154,188,227,194,157,195,74,186,206,130,198,73,212,60,204,122,222,99,205,196,229,213,83,230,108,171,126,192,104,216,207,117,217,197,214,79,207,110,221,79,217,144,206,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165]
+[178,205,218,148,184,163,221,185,200,228,172,155,210,222,88,206,226,67,132,212,91,206,104,212,174,205,132,159,230,175,216,198,227,190,212,198,122,213,169,204,92,197,118,191,191,224,69,219,197,72,218,77,175,111,155,217,220,170,231,91,221,217,95,146,177,123,195,205,151,209,207,36,202,200,226,176,232,53,167,199,89,184,213,104,154,153,216,214,215,174,205,72,211,78,221,212,232,223,73,158,220,158,202,222,189,165,205,175,222,132,126,179,219,110,209,158,208,98,176,192,226,34,158,205,126,178,224,182,227,100,152,191,169,195,163,172,208,117,199,217,167,217,157,163,194,217,200,217,23,221,209,146,150,204,200,125,215,232,68,147,212,41,223,178,152,173,210,139,198,182,196,207,95,176,205,223,83,216,85,207,210,52,177,178,230,197,119,226,99,182,210,212,77,138,199,123,179,111,219,69,223,65,204,215,83,197,87,211,132,216,135,178,157,166,216,85,170,195,208,190,175,134,220,200,67,221,211,66,227,222,226,190,209,205,67,207,139,208,127,186,205,168,221,179,223,117,148,221,216,80,189,125,199,202,64,218,77,195,190,221,181,98,143,214,220,97,187,127,219,122,216,87,138,212,194,112,219,227,101,220,100,164,234,109,221,102,223,89,184,205,219,77,188,223,172,171,175,152,175,137,213,197,114,205,221,138,181,174,227,73,147,144,178,147,215,152,182,204,80,210,123,211,121,209,224,224,219,211,163,133,187,148,151,163,221,94,133,213,72,187,224,216,162,154,224,184,118,204,220,154,117,220,162,202,223,195,110,197,151,224,88,182,217,221,214,118,218,118,164,205,97,221,183,154,206,197,74,170,219,103,230,215,192,224,78,184,72,201,227,221,191,181,104,190,224,221,99,123,206,102,228,202,74,195,96,225,176,232,231,96,225,206,61,173,101,190,211,90,213,199,203,184,209,173,160,207,203,99,201,195,132,195,214,148,211,227,192,215,212,127,162,213,114,178,111,207,71,129,182,212,75,176,209,137,213,224,220,70,232,116,228,179,200,184,222,93,202,71,219,123,213,119,204,173,135,118,207,96,216,107,210,202,189,172,211,76,146,195,169,83,227,62,219,223,125,158,202,226,98,214,225,76,156,211,204,110,190,224,200,64,228,223,72,213,102,214,178,219,128,211,127,187,213,160,216,134,202,150,186,201,195,230,199,206,193,205,219,216,84,213,223,192,222,109,187,209,79,153,204,224,72,167,190,183,183,228,133,227,103,197,225,203,205,201,207,213,210,78,138,195,133,195,180,190,185,207,189,100,191,229,198,129,226,135,205,164,226,190,73,200,231,209,216,147,221,228,165,213,209,181,157,222,117,216,58,148,219,92,222,219,63,191,218,186,197,158,172,204,109,204,102,223,229,91,192,217,186,204,144,173,209,95,186,222,225,178,183,222,71,193,182,232,63,227,228,82,209,162,219,183,222,122,212,195,60,219,130,223,69,216,111,198,224,80,223,74,164,148,227,193,216,139,207,198,148,227,199,169,230,225,86,138,165,206,91,213,164,215,229,173,216,210,92,166,205,102,196,78,202,74,187,202,229,191,222,57,206,115,183,94,225,173,198,109,212,224,126,215,44,221,80,196,95,177,218,217,198,109,218,191,221,214,227,71,207,123,206,90,219,218,108,181,214,123,150,204,214,208,225,199,191,192,213,93,217,149,178,199,106,204,80,208,60,131,132,232,218,213,115,202,94,175,227,217,114,200,222,178,180,185,192,193,205,173,226,217,222,197,184,205,211,136,214,221,62,213,89,169,184,135,198,98,216,225,71,230,133,213,154,216,189,122,224,108,204,88,207,227,93,191,204,214,66,153,219,209,203,218,237,229,222,148,174,218,107,214,169,223,216,173,220,148,199,188,175,203,142,188,210,98,167,174,224,103,156,97,220,111,216,70,130,221,216,115,166,164,162,210,216,69,173,203,77,216,175,172,210,98,199,213,88,221,82,184,131,201,220,133,211,116,156,219,213,227,128,221,237,172,217,64,180,221,70,169,220,216,134,215,177,190,197,229,221,226,74,189,190,219,86,222,74,213,106,184,182,205,221,229,143,201,198,162,209,128,221,209,60,216,146,228,106,215,154,214,202,82,194,141,168,217,213,80,140,114,220,149,229,215,150,211,170,199,109,218,108,206,125,204,164,183,148,193,153,188,143,160,199,210,124,225,105,178,75,222,112,205,75,199,83,222,215,158,104,205,70,215,223,158,220,99,124,194,91,219,65,209,149,234,188,222,218,200,66,208,101,208,188,201,207,198,160,205,219,118,170,195,141,128,182,189,211,51,179,216,112,152,196,224,80,223,177,218,127,186,113,151,117,159,184,200,231,212,134,222,68,216,204,90,151,202,158,173,146,144,209,222,89,171,188,206,215,117,191,223,80,215,68,151,159,210,74,208,105,204,105,174,228,67,147,202,214,41,222,163,183,142,227,188,200,208,132,157,191,195,94,166,201,155,214,132,185,195,165,214,219,156,217,100,147,220,105,220,223,225,117,176,166,207,134,218,202,172,183,217,76,229,139,217,87,208,181,214,169,154,199,231,208,200,157,213,222,49,207,191,139,219,218,82,122,154,137,190,176,192,180,231,80,208,136,185,112,189,187,192,208,219,110,208,102,186,225,220,218,235,214,96,133,198,199,95,152,83,219,190,124,148,222,213,207,145,217,168,200,32,208,198,171,205,218,104,218,136,230,77,139,178,212,214,129,211,233,93,221,95,155,153,221,151,201,108,193,216,172,208,72,177,217,208,225,60,81,172,234,180,146,184,180,199,222,224,182,106,183,105,221,208,81,221,137,211,44,198,224,205,82,187,164,147,213,163,229,192,219,218,190,228,221,218,116,231,97,182,126,213,193,173,207,103,207,122,161,156,193,153,192,92,198,155,223,224,106,223,170,177,225,40,206,203,221,152,208,116,203,105,197,221,65,226,111,224,215,215,142,154,211,84,184,135,169,212,213,76,229,225,130,207,85,209,177,116,140,150,229,209,105,164,186,214,219,62,148,203,230,118,207,216,217,211,114,201,134,217,87,197,124,209,186,184,164,189,163,212,85,219,106,209,220,168,221,212,112,189,183,229,200,192,106,224,114,220,95,141,223,222,219,226,79,187,202,212,222,193,128,223,209,160,178,197,222,111,152,114,164,206,179,189,183,122,212,149,150,130,214,115,195,118,204,208,120,195,163,159,220,125,222,174,211,144,140,220,80,173,204,87,215,149,227,97,229,221,119,213,139,212,82,215,68,213,136,186,168,202,85,213,93,168,218,63,222,121,213,90,218,94,197,221,182,220,112,224,221,83,215,194,138,181,197,221,91,186,194,99,179,213,210,76,188,214,216,79,213,92,220,94,201,108,213,131,221,153,219,74,159,172,218,217,219,108,209,35,217,112,157,206,148,211,126,214,107,191,105,164,224,220,126,204,201,211,119,203,129,165,205,130,218,137,211,178,204,215,206,119,209,75,193,182,115,178,207,217,220,184,199,167,189,230,140,208,167,173,195,136,220,205,64,186,159,194,75,213,189,110,189,208,120,176,182,162,196,183,137,134,198,172,193,184,172,212,189,214,200,95,167,199,110,195,116,180,204,66,217,126,222,226,193,166,143,121,218,224,196,140,226,142,206,66,214,140,218,144,201,221,152,204,154,221,101,225,69,216,212,128,181,200,180,206,136,193,226,226,219,141,168,202,209,105,178,128,208,197,145,163,218,124,220,147,220,156,211,69,212,226,132,223,165,211,110,229,194,193,204,208,90,218,106,152,215,134,180,182,204,211,80,211,108,204,102,208,102,192,94,191,206,203,140,165,213,197,105,186,186,210,62,208,165,213,174,186,115,211,164,193,128,201,169,217,167,217,174,222,167,129,170,217,218,182,159,113,208,85,219,126,219,89,172,213,212,69,205,135,124,205,67,208,52,186,160,225,125,190,65,227,168,180,166,151,194,205,88,138,204,110,124,223,80,193,106,179,183,163,220,148,205,218,200,221,198,202,190,213,87,227,239,81,213,115,211,219,201,204,105,216,83,210,95,170,219,197,86,228,86,209,125,231,83,198,194,171,144,183,222,216,216,226,204,109,216,115,180,227,116,207,223,84,190,215,117,212,79,214,230,218,134,208,92,209,99,181,220,61,212,85,199,185,174,171,220,221,57,146,224,214,81,183,218,152,163,193,72,203,111,130,222,204,217,229,209,201,189,220,213,72,192,214,221,157,197,124,209,100,223,162,223,73,85,221,68,210,46,205,76,118,160,180,200,85,218,34,196,170,204,88,168,224,71,188,207,166,181,210,101,148,215,76,219,217,104,199,180,210,208,69,201,202,216,224,89,206,88,128,125,196,203,106,183,182,84,228,200,120,171,166,153,215,210,82,210,65,163,131,213,83,180,205,92,152,228,99,212,212,209,94,215,218,95,206,179,236,219,204,102,196,110,160,218,140,228,102,218,177,172,193,95,209,215,203,220,78,213,191,189,168,204,218,192,89,213,223,208,230,168,202,169,222,56,155,203,73,197,156,208,222,127,212,223,160,217,201,223,205,217,100,221,208,143,218,22,208,224,86,209,90,220,144,182,139,219,79,194,183,225,216,209,190,102,221,220,203,152,215,113,230,106,212,221,106,197,111,185,211,83,162,200,208,219,124,210,92,161,216,221,79,208,166,211,142,179,222,197,79,185,226,182,193,195,170,202,113,192,231,228,197,180,152,223,155,215,97,213,85,205,145,200,89,209,201,139,217,163,133,206,174,112,205,213,217,123,201,136,132,221,206,105,200,139,183,160,215,134,221,208,181,208,101,210,225,116,183,217,80,186,120,162,194,201,97,188,209,174,122,229,214,205,120,218,228,228,83,134,201,119,171,210,55,227,107,225,64,174,203,130,213,226,95,206,157,139,154,171,139,224,219,154,190,206,218,217,221,86,135,217,213,183,118,187,186,128,212,79,184,186,156,188,134,216,172,155,195,210,176,223,125,128,217,213,214,217,94,189,192,133,167,85,187,217,60,158,221,122,217,117,224,95,206,89,199,74,205,210,70,209,83,196,77,169,188,228,114,188,105,229,182,174,223,59,181,205,127,225,222,128,190,219,112,197,115,222,205,115,184,214,210,238,196,212,85,207,86,183,215,178,187,112,194,176,212,90,217,80,164,179,187,138,221,214,203,96,227,198,82,209,207,96,208,76,228,224,205,207,186,178,221,224,189,143,229,182,229,132,213,201,186,218,67,222,221,83,216,44,220,68,214,103,137,210,48,210,211,88,222,169,225,155,181,200,207,62,219,152,179,130,215,86,183,90,202,164,179,205,128,219,224,148,190,117,213,221,184,175,146,223,216,82,230,92,201,231,218,172,207,209,189,150,216,142,226,46,197,219,92,228,168,216,111,221,204,216,190,180,189,206,108,204,101,224,74,223,65,181,142,211,203,122,220,214,66,179,185,222,116,120,209,222,101,172,209,232,56,223,177,212,107,191,168,193,193,105,212,105,163,209,191,117,194,170,211,190,121,221,90,161,207,67,226,90,216,60,171,219,89,220,184,214,194,144,182,214,208,196,133,226,214,77,190,215,63,115,208,92,227,204,68,197,77,178,188,204,216,202,194,152,175,181,208,131,196,223,128,177,116,207,102,220,121,216,199,202,207,74,211,196,217,177,214,218,221,93,219,192,215,143,139,183,226,83,102,219,211,100,194,47,119,204,160,143,180,149,183,226,96,172,212,186,198,207,182,207,221,164,146,224,61,200,140,147,196,214,60,226,215,111,216,186,180,161,234,196,124,208,214,221,189,178,112,168,208,136,169,181,202,124,201,74,211,143,192,175,119,163,202,67,191,172,148,179,223,122,217,117,180,190,186,209,72,193,182,218,80,156,138,201,202,218,208,149,190,109,224,64,192,114,219,199,210,208,97,232,205,197,188,189,87,213,152,196,164,131,225,94,219,205,163,191,172,196,189,206,110,201,73,191,122,210,173,208,73,221,136,222,185,224,74,213,86,185,221,170,210,69,165,156,210,102,211,210,221,197,209,154,127,212,180,208,139,231,207,50,166,96,150,158,206,228,214,202,95,162,220,191,136,217,54,155,201,140,179,191,91,213,220,74,145,216,232,45,208,217,209,182,160,182,100,221,155,219,227,160,180,209,147,174,212,67,209,82,213,148,208,51,176,88,210,71,117,174,218,82,211,188,170,210,186,136,176,220,157,189,167,190,212,160,212,135,201,219,49,162,165,209,117,175,213,152,176,220,221,124,150,204,220,73,228,194,218,57,195,173,159,173,175,206,176,229,79,164,201,203,152,214,116,137,219,66,222,214,80,175,202,90,225,113,219,206,78,190,89,214,50,209,154,188,227,194,157,195,74,186,206,130,198,73,212,60,204,122,222,99,205,196,229,213,83,230,108,171,126,192,104,216,207,117,217,197,214,79,207,110,221,79,217,144,206,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64]

ivf.pid.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a9c207115138b732fedd24d34a2a74da2ab3028ed30a69aed5a9aa7e81132523
-size 1498456
+oid sha256:d5ad00d4796fd5a520dcd4b80c7ec7a5f9634b9b6e56454dbd2983f38ab044ea
+size 1499416

metadata.json

@@ -37,7 +37,7 @@
     "checkpoint":"colbert-ir/colbertv2.0",
     "triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
     "collection":[
-      "list with 3328 elements starting with...",
+      "list with 3334 elements starting with...",
       [
         "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
         "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
     "root":".ragatouille/",
     "experiment":"colbert",
     "index_root":null,
-    "name":"2024-04/25/18.54.51",
+    "name":"2024-04/26/02.54.08",
     "rank":0,
     "nranks":1,
     "amp":true,
@@ -59,8 +59,8 @@
   },
   "num_chunks":1,
   "num_partitions":8192,
-  "num_embeddings":572442,
-  "avg_doclen":172.0078125,
+  "num_embeddings":573356,
+  "avg_doclen":171.9724055189,
   "RAGatouille":{
     "index_config":{
       "index_type":"PLAID",

pid_docid_map.json

@@ -3326,5 +3326,11 @@
   "3324":"2404.15778",
   "3325":"2404.16030",
   "3326":"2404.15653",
-  "3327":"2404.16035"
+  "3327":"2404.16035",
+  "3328":"2404.16510",
+  "3329":"2404.16510",
+  "3330":"2404.16790",
+  "3331":"2404.16790",
+  "3332":"2404.16821",
+  "3333":"2404.16821"
 }

plan.json

@@ -37,7 +37,7 @@
         "checkpoint": "colbert-ir\/colbertv2.0",
         "triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
         "collection": [
-            "list with 3328 elements starting with...",
+            "list with 3334 elements starting with...",
             [
                 "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
                 "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
         "root": ".ragatouille\/",
         "experiment": "colbert",
         "index_root": null,
-        "name": "2024-04\/25\/18.54.51",
+        "name": "2024-04\/26\/02.54.08",
         "rank": 0,
         "nranks": 1,
         "amp": true,
@@ -59,6 +59,6 @@
     },
     "num_chunks": 1,
     "num_partitions": 8192,
-    "num_embeddings_est": 572442.0,
-    "avg_doclen_est": 172.0078125
+    "num_embeddings_est": 573356.0219726562,
+    "avg_doclen_est": 171.972412109375
 }