lightonai

Full BF16 version of the model. We recommend this variant for inference and further fine-tuning. LightOnOCR-1B is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs. [](https://colab.research.google.com/#fileId=https%3A//huggingface.co/lightonai/LightOnOCR-1B-1025/blob/main/notebook.ipynb) 📝 Read the full blog post | 🚀 Try the demo | 📓 Finetuning notebook ⚡ Speed: 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR 💸 Efficiency: Processes 5.71 pages/s on a single H100 (~493k pages/day) for <$0.01 per 1,000 pages 🧠 End-to-End: Fully differentiable, no external OCR pipeline 🧾 Versatile: Handles tables, receipts, forms, multi-column layouts, and math notation 🌍 Compact variants: 32k and 16k vocab options for European languages LightOnOCR combines a Vision Transformer encoder(Pixtral-based) with a lightweight text decoder(Qwen3-based) distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages. | Model | ArXiv | Old Scans | Math | Tables | Multi-Column | Tiny Text | Base | Overall | | :----------------- | :---: | :-------: | :--: | :----: | :----------: | :-------: | :--: | :-----: | | LightOnOCR-1B-1025 (151k vocab) | 81.4 | 71.6 | 76.4 | 35.2 | 80.0 | 88.7 | 99.5 | 76.1 | | LightOnOCR-1B-32k (32k vocab) | 80.6 | 66.2 | 73.5 | 33.5 | 71.2 | 87.6 | 99.5 | 73.1 | | LightOnOCR-1B-16k (16k vocab) | 82.3 | 72.9 | 75.3 | 33.5 | 78.6 | 85.1 | 99.8 | 75.4 | All benchmarks evaluated using vLLM on the Olmo-Bench. [2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀 Render PDFs to PNG or JPEG at a target longest dimension of 1540px Maintain aspect ratio to preserve text geometry Use one image per page; batching supported by vLLM | Variant | Description | | :--------------------------------------------------------------------------------- | :-------------------------------------------- | | LightOnOCR-1B-1025 | Full multilingual model (default) | | LightOnOCR-1B-32k | Fastest pruned-vocabulary version (32k tokens) optimized for European languages | | LightOnOCR-1B-16k | Most compact variant with smallest vocabulary | Transformers integration is coming soon for training and inference. LoRA fine-tuning Domain adaptation (receipts, scientific articles, forms, etc.) Multilingual fine-tuning with task-specific corpora Trained on a diverse large-scale PDF corpus covering: Scientific papers, books, receipts, invoices, tables, forms, and handwritten text Multiple languages (Latin alphabet dominant) Real and synthetic document scans The dataset will be released under an open license.

Reason-ModernColBERT Reason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. License Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. PyLate model based on lightonai/GTE-ModernColBERT-v1 This is a PyLate model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. Model Description - Model Type: PyLate model - Base model: lightonai/GTE-ModernColBERT-v1 - Document Length: 8192 tokens - Query Length: 128 tokens - Output Dimensionality: 128 tokens - Similarity Function: MaxSim - Training Dataset: - reasonir-hq - Language: en - Documentation: PyLate Documentation - Repository: PyLate on GitHub - Hugging Face: PyLate models on Hugging Face PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. First, load the ColBERT model and initialize the Voyager index, then encode and index your documents: Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: Evaluation BRIGHT Benchmark The BRIGHT benchmark is aimed at evaluating reasoning-intensive retrieval performance. Reason-ModernColBERT outperforms all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction compared to usual dense (single vector) retrieval performed by other models as highlighted in the next section. | Model / Metric | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem - Q | Theorem - T | Mean StackExchange | Mean coding | Mean theorem | Full mean | |----------------------------------------------------------|---------|-------|-----------|------------|----------|---------------|-------------|----------|------|------|-----------|-----------|-------------------|-------------|--------------|-----------| | BM25 | 18.9 | 27.2 | 14.9 | 12.5 | 13.6 | 18.4 | 15 | 24.4 | 7.9 | 6.2 | 10.4 | 4.9 | 17.21 | 16.15 | 7.17 | 14.53 | | 1B OS | | | | | | | | | | | | | | | | | | E5 | 18.6 | 26 | 15.5 | 15.8 | 16.3 | 11.2 | 18.1 | 28.7 | 4.9 | 7.1 | 26.1 | 26.8 | 17.36 | 16.8 | 20 | 17.93 | | SFR | 19.1 | 26.7 | 17.8 | 19 | 16.3 | 14.4 | 19.2 | 27.4 | 2 | 7.4 | 24.3 | 26 | 18.93 | 14.7 | 19.23 | 18.3 | | Inst-XL | 21.6 | 34.3 | 22.4 | 27.4 | 18.2 | 21.2 | 19.1 | 27.5 | 5 | 8.5 | 15.6 | 5.9 | 23.46 | 16.25 | 10 | 18.89 | | GritLM | 24.8 | 32.3 | 18.9 | 19.8 | 17.1 | 13.6 | 17.8 | 29.9 | 22 | 8.8 | 25.2 | 21.2 | 20.61 | 25.95 | 18.4 | 20.95 | | Qwen | 30.6 | 36.4 | 17.8 | 24.6 | 13.2 | 22.2 | 14.8 | 25.5 | 9.9 | 14.4 | 27.8 | 32.9 | 22.8 | 17.7 | 25.03 | 22.51 | | Proprietary | | | | | | | | | | | | | | | | | | Cohere | 18.7 | 28.4 | 20.4 | 21.6 | 16.3 | 18.3 | 17.6 | 26.8 | 1.9 | 6.3 | 15.7 | 7.2 | 20.19 | 14.35 | 9.73 | 16.6 | | OpenAI | 23.3 | 26.7 | 19.5 | 27.6 | 12.8 | 14.3 | 20.5 | 23.6 | 2.4 | 8.5 | 23.5 | 11.7 | 20.67 | 13 | 14.57 | 17.87 | | Voyage | 23.1 | 25.4 | 19.9 | 24.9 | 10.8 | 16.8 | 15.4 | 30.6 | 1.5 | 7.5 | 27.4 | 11.6 | 19.47 | 16.05 | 15.5 | 17.91 | | Google | 22.7 | 34.8 | 19.6 | 27.8 | 15.7 | 20.1 | 17.1 | 29.6 | 3.6 | 9.3 | 23.8 | 15.9 | 22.54 | 16.6 | 16.33 | 20 | ReasonIR data | ReasonIR-8B | 26.2 | 31.4 | 23.3 | 30 | 18 | 23.9 | 20.5 | 35 | 10.5 | 14.7 | 31.9 | 27.2 | 24.76 | 22.75 | 24.6 | 24.38 | | Reason-ModernColBERT (150M) | 33.25 | 41.02 | 24.93 | 30.73 | 21.12 | 20.62 | 20.31 | 31.07 | 8.51 | 9.17 | 19.51 | 11.24 | 27.43 | 19.79 | 15.38 | 22.62 | Comparison with a dense model A fair claim would be that the performance of Reason-ModernColBERT are mostly due to the ReasonIR data. Although the differences between ReasonIR-8B and Reason-ModernColBERT already hint that it is most likely more than just that, we conducted a small experiment by training a dense (single vector) model in the same setup using Sentence Transformers as a multi-vector one trained using PyLate. This experiment highlights a very large gap in performance. Obviously, more rigourous experiments are required to draw conclusion (e.g, both models could have been further tuned and the training could have been enhanced (e.g, we did not gather negatives from other GPUs in these experiments because ST do not supports it for now)) but the gap seems really big and it does correlate pretty well with Reason-ModernColBERT being competitive with ReasonIR-8B while being more than 50 times smaller. | Model/Split | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem Q | Theorem T | Mean StackExchange | Mean coding | Mean theorem | Full mean | | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | | Dense (single vector) model | 7.51 | 16.92 | 13.43 | 17.18 | 10.23 | 8.93 | 8.85 | 24.88 | 1.43 | 9.81 | 18.83 | 9.71 | 11.86 | 13.16 | 12.78 | 12.31 | | Late-interaction (multi vector model) | 28.02 | 39.25 | 21.51 | 27.05 | 19.86 | 17.23 | 21.1 | 27.37 | 3.76 | 6.87 | 16.06 | 7.21 | 24.86 | 15.57 | 10.05 | 19.61 | GPT4 reasoning trace Although those models are able to do some reasoning-intensive matching, it has been shown that they greatly benefits from using the reasoning trace/query reformulation from a LLM such as GPT4. Here is the results of Reason-ModernColBERT on this setup: | Model | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem Q | Theorem T | Moyenne Stack | Mean Code | Mean Theorem | Full mean | |------------------------------------|---------|--------|-----------|------------|----------|---------------|-------------|----------|-------|------|-----------|-----------|----------------|-----------|--------------|----------------| | BM25 | 53.6 | 53.6 | 24.3 | 38.6 | 18.8 | 22.7 | 25.9 | 19.3 | 17.7 | 3.9 | 20.2 | 18.9 | 33.93 | 18.5 | 19.55 | 26.46 | | Contriever | 37.5 | 40.5 | 22.6 | 27.1 | 15.2 | 22.6 | 19.6 | 22.5 | 13.8 | 8.1 | 24.1 | 16.2 | 26.44 | 18.15 | 20.15 | 22.48 | | GritLM-7B | 33.2 | 33 | 23.3 | 30.6 | 15.2 | 17.5 | 21.7 | 33.2 | 11.7 | 6.8 | 26.9 | 28 | 24.93 | 22.45 | 27.45 | 23.425 | | RankLLaMA-7B (top-100) | 17.5 | 15.5 | 13.1 | 13.6 | 17.9 | 6.9 | 16.9 | 8.4 | 46.8 | 2.2 | 4.5 | 3.5 | 14.49 | 27.6 | 4 | 13.9 | | Rank1-7B (top-100) | 48.8 | 36.7 | 20.8 | 35 | 22 | 18.7 | 36.2 | 12.7 | 31.2 | 6.3 | 23.7 | 37.8 | 31.17 | 21.95 | 30.75 | 27.49 | | Rank1-32B (top-100) | 49.7 | 35.8 | 22 | 37.5 | 22.5 | 21.7 | 35 | 18.8 | 32.5 | 10.8 | 22.9 | 43.7 | 32.03 | 25.65 | 33.3 | 29.41 | | ReasonIR-8B | 43.6 | 42.9 | 32.7 | 38.8 | 20.9 | 25.8 | 27.5 | 31.5 | 19.6 | 7.4 | 33.1 | 35.7 | 33.17 | 25.55 | 34.4 | 29.96 | | Reason-ModernColBERT | 61.54 | 56.79 | 26.2 | 43.79 | 20.76 | 31.61 | 29.12 | 27.46 | 8.31 | 8.26 | 26.46 | 23.07 | 38.54 | 17.885 | 24.765 | 30.28 | As highlighted by these results, Reason-ModernColBERT benefits greatly from using those traces (+7.66 NDCG@10 in average) even more than ReasonIR-8B (+5.58). It thus reaches top-1 by closing the gap and outperforms it on this setup, as well as outperforming methods based on reranking with 7B models. However, it should be noted that these experiments also highlighted that Reason-ModernColBERT does not scale very well to very large queries (while ColBERT models are known to generalize very well to large documents), most probably due to the assymetric nature of the MaxSim operator. This prevent the model from leveraging the full query rewriting and reasoning trace from the LLM. Training the model on longer and more diverse lengths of queries, such as in the VL split of the ReasonIR data is a promising avenue to better leverage these extensive queries/reasoning trace. In an effort of transparency, please note that, contrary to the main results that have been evaluated with mostly the same query length (except Pony), those results have been obtained by sweeping over query lengths, here are the query lengths used for each split: | Query | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem Q | Theorem T | |---------------|---------|-------|-----------|------------|----------|---------------|-------------|----------|------|------|-----------|-----------| | Query length | 256 | 1024 | 128 | 128 | 256 | 256 | 128 | 128 | 128 | 256 | 256 | 128 | Dataset: train at 0275f82 Size: 100,521 training samples Columns: query , pos , and neg Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | min: 38 tokens mean: 97.84 tokens max: 128 tokens | min: 85 tokens mean: 127.63 tokens max: 128 tokens | min: 81 tokens mean: 127.77 tokens max: 128 tokens | Samples: | query | pos | neg | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Given this reasoning-intensive query, find relevant documents that could help answer the question. A researcher is analyzing a sound signal represented by the equation f(t) = 2sin(3πt) + sin(5πt) + 0.5sin(7πt). Using the Fourier transform, what are the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal? | A sound signal is given by the equation f(t) = sin(2πt) + sin(4πt) + sin(6πt) where t is time in seconds. Use Fourier transform to find the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal. To find the frequencies, amplitudes, and phases of the individual sinusoidal components in the signal f(t) = sin(2πt) + sin(4πt) + sin(6πt), we can use the Fourier transform. The Fourier transform of a continuous function f(t) is given by: F(ω) = ∫[f(t) e^(-jωt)] dt where F(ω) is the Fourier transform of f(t), ω is the angular frequency, and j is the imaginary unit (j^2 = -1). In this case, f(t) is already given as a sum of sinusoidal functions, so we can directly identify the frequencies, amplitudes, and phases of the individual components. 1. First component: sin(2πt) - Frequency: The angular frequency is 2π, so the frequency is ω/(2π) = 1 Hz. - Amplitude: The coefficient of the sine function is 1, so the amplitude is 1. - Phase: There is no phase shi... | The Fourier transform is widely used in various fields, including engineering, physics, and data analysis. It is a powerful tool for decomposing a signal into its constituent frequencies. In music, for example, the Fourier transform can be used to analyze the frequency components of a sound wave. By applying the Fourier transform to a sound signal, one can identify the different frequencies present in the signal, as well as their relative amplitudes. This information can be useful in a variety of applications, such as sound filtering and audio processing. The Fourier transform can also be used to analyze images and other types of data. In image processing, the Fourier transform can be used to filter out noise and other unwanted features from an image. It can also be used to compress images by representing them in the frequency domain. In addition to its many practical applications, the Fourier transform also has a number of interesting theoretical properties. For example, it has been ... | | Given this reasoning-intensive query, find relevant documents that could help answer the question. A manufacturer is designing a cone-shaped container with a fixed volume of 200π cubic centimeters. The container's height is 12 centimeters, and the radius of the base is unknown. If the manufacturer wants to minimize the surface area of the container while maintaining its volume, what should be the radius of the base? | A right circular cone has a radius of 6cm and a slant height of 10cm. Determine the surface area of the cone. To find the surface area of a right circular cone, we need to calculate the area of the base and the lateral surface area, and then add them together. The base of the cone is a circle with radius r = 6 cm. The area of the base (Abase) can be found using the formula for the area of a circle: Abase = πr^2 Abase = π(6 cm)^2 Abase = 36π cm^2 The lateral surface area (Alateral) can be found using the formula for the lateral surface area of a cone: Alateral = πrs, where r is the radius and s is the slant height. Given that the slant height s = 10 cm, we can calculate the lateral surface area: Alateral = π(6 cm)(10 cm) Alateral = 60π cm^2 Now, we can find the total surface area (Atotal) by adding the base area and the lateral surface area: Atotal = Abase + Alateral Atotal = 36π cm^2 + 60π cm^2 Atotal = 96π cm^2 The surface area of the cone is 96π cm^2. | Torus-Shaped Containers in Chemical Engineering - New Designs and ApplicationsTorus-shaped containers are commonly used in chemical engineering for storing and transporting fluids. These containers have a distinctive doughnut shape, with a central hole and a circular cross-section. In this article, we will explore the design and applications of torus-shaped containers in chemical engineering.One of the main advantages of torus-shaped containers is their high volume-to-surface-area ratio. This makes them ideal for storing large quantities of fluids while minimizing the amount of material needed for construction. Additionally, the curved shape of the container provides added strength and stability, making it less prone to rupture or leakage.The design of torus-shaped containers typically involves the use of computer-aided design (CAD) software to create detailed models of the container's geometry. Engineers can then use these models to simulate various scenarios, such as fluid flow and ... | | Given this reasoning-intensive query, find relevant documents that could help answer the question. On the xy-coordinate plane, points A and B are given as A(2, 4) and B(8, -3). Determine the coordinates of the point on line segment AB that is three times as far from A as it is from B. | On the xy co-ordinate plane, point C is (5,-2) and point D is (-1,1.5). The point on line segment CD that is twice as far from C as from D is: Answer Choices: (A) (1,-1) (B) (1,1) (C) (2,0.25) (D) (3,0.5) (E) (3,1) Let's think about the multi-choice question step by step. We want the point on the line that is twice as far from C as it is from D. We can examine the x and y coordinates separately since they are independent. It should be noted that there are two solutions to this problem, one point between C and D, and another point with D in the middle of C and the point. We can quickly look at the answer choices and see that all the points are between C and D, therefore we can search for that point using the following method: Taking the x-coordinate first, the distance between C and D is |(x-coordinate ofC - (x-coordinate ofD|= |5 - (-1)| = 6 The x-coordinate that is twice as far from C as it is from D (and in between C andD will be 4 units from C and 2 units from D. So the ... | The concept of midpoint is often useful in various mathematical problems, but sometimes we need to find other points that divide a line segment in a particular ratio. One common scenario is when we need to find the point that divides the line segment in the ratio of the other two points. Let's consider an example to understand this better. Suppose we have two points E(3, 4) and F(7, -2) on the xy-coordinate plane, and we want to find the point G on the line segment EF such that EG:GF = 2:5. To solve this problem, we can use the concept of section formula, which states that if a point P(x, y) divides the line segment joining the points A(x1, y1) and B(x2, y2) in the ratio m:n, then the coordinates of P are ((mx2+nx1)/(m+n), (my2+ny1)/(m+n)). Using this formula, we can find the coordinates of point G. First, we need to find the difference in x-coordinates and y-coordinates of points E and F. The difference in x-coordinates is 7 - 3 = 4, and the difference in y-coordinates is -2 - 4 = -6... | Loss: pylate.losses.cachedcontrastive.CachedContrastive Training Hyperparameters Non-Default Hyperparameters - `perdevicetrainbatchsize`: 256 - `perdeviceevalbatchsize`: 256 - `learningrate`: 1e-05 - `bf16`: True - `dataloadernumworkers`: 8 - `overwriteoutputdir`: False - `dopredict`: False - `evalstrategy`: no - `predictionlossonly`: True - `perdevicetrainbatchsize`: 256 - `perdeviceevalbatchsize`: 256 - `pergputrainbatchsize`: None - `pergpuevalbatchsize`: None - `gradientaccumulationsteps`: 1 - `evalaccumulationsteps`: None - `torchemptycachesteps`: None - `learningrate`: 1e-05 - `weightdecay`: 0.0 - `adambeta1`: 0.9 - `adambeta2`: 0.999 - `adamepsilon`: 1e-08 - `maxgradnorm`: 1.0 - `numtrainepochs`: 3 - `maxsteps`: -1 - `lrschedulertype`: linear - `lrschedulerkwargs`: {} - `warmupratio`: 0.0 - `warmupsteps`: 0 - `loglevel`: passive - `loglevelreplica`: warning - `logoneachnode`: True - `loggingnaninffilter`: True - `savesafetensors`: True - `saveoneachnode`: False - `saveonlymodel`: False - `restorecallbackstatesfromcheckpoint`: False - `nocuda`: False - `usecpu`: False - `usempsdevice`: False - `seed`: 42 - `dataseed`: None - `jitmodeeval`: False - `useipex`: False - `bf16`: True - `fp16`: False - `fp16optlevel`: O1 - `halfprecisionbackend`: auto - `bf16fulleval`: False - `fp16fulleval`: False - `tf32`: None - `localrank`: 0 - `ddpbackend`: None - `tpunumcores`: None - `tpumetricsdebug`: False - `debug`: [] - `dataloaderdroplast`: False - `dataloadernumworkers`: 8 - `dataloaderprefetchfactor`: None - `pastindex`: -1 - `disabletqdm`: False - `removeunusedcolumns`: True - `labelnames`: None - `loadbestmodelatend`: False - `ignoredataskip`: False - `fsdp`: [] - `fsdpminnumparams`: 0 - `fsdpconfig`: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgradckpt': False} - `fsdptransformerlayerclstowrap`: None - `acceleratorconfig`: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None} - `deepspeed`: None - `labelsmoothingfactor`: 0.0 - `optim`: adamwtorch - `optimargs`: None - `adafactor`: False - `groupbylength`: False - `lengthcolumnname`: length - `ddpfindunusedparameters`: None - `ddpbucketcapmb`: None - `ddpbroadcastbuffers`: False - `dataloaderpinmemory`: True - `dataloaderpersistentworkers`: False - `skipmemorymetrics`: True - `uselegacypredictionloop`: False - `pushtohub`: False - `resumefromcheckpoint`: None - `hubmodelid`: None - `hubstrategy`: everysave - `hubprivaterepo`: None - `hubalwayspush`: False - `gradientcheckpointing`: False - `gradientcheckpointingkwargs`: None - `includeinputsformetrics`: False - `includeformetrics`: [] - `evaldoconcatbatches`: True - `fp16backend`: auto - `pushtohubmodelid`: None - `pushtohuborganization`: None - `mpparameters`: - `autofindbatchsize`: False - `fulldeterminism`: False - `torchdynamo`: None - `rayscope`: last - `ddptimeout`: 1800 - `torchcompile`: False - `torchcompilebackend`: None - `torchcompilemode`: None - `dispatchbatches`: None - `splitbatches`: None - `includetokenspersecond`: False - `includenuminputtokensseen`: False - `neftunenoisealpha`: None - `optimtargetmodules`: None - `batchevalmetrics`: False - `evalonstart`: False - `useligerkernel`: False - `evalusegatherobject`: False - `averagetokensacrossdevices`: False - `prompts`: None - `batchsampler`: batchsampler - `multidatasetbatchsampler`: proportional | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0025 | 1 | 4.9684 | | 0.0051 | 2 | 4.6956 | | 0.0076 | 3 | 4.5076 | | 0.0102 | 4 | 4.3723 | | 0.0127 | 5 | 4.3305 | | 0.0153 | 6 | 4.0355 | | 0.0178 | 7 | 3.7886 | | 0.0204 | 8 | 3.6133 | | 0.0229 | 9 | 3.2395 | | 0.0254 | 10 | 3.1481 | | 0.0280 | 11 | 2.7444 | | 0.0305 | 12 | 2.4946 | | 0.0331 | 13 | 2.333 | | 0.0356 | 14 | 2.2471 | | 0.0382 | 15 | 1.9117 | | 0.0407 | 16 | 1.6753 | | 0.0433 | 17 | 1.2413 | | 0.0458 | 18 | 1.1201 | | 0.0483 | 19 | 1.0335 | | 0.0509 | 20 | 1.0583 | | 0.0534 | 21 | 1.067 | | 0.0560 | 22 | 0.7056 | | 0.0585 | 23 | 0.761 | | 0.0611 | 24 | 0.5501 | | 0.0636 | 25 | 0.6486 | | 0.0662 | 26 | 0.4639 | | 0.0687 | 27 | 0.3885 | | 0.0712 | 28 | 0.4982 | | 0.0738 | 29 | 0.4784 | | 0.0763 | 30 | 0.5189 | | 0.0789 | 31 | 0.4824 | | 0.0814 | 32 | 0.4183 | | 0.0840 | 33 | 0.4945 | | 0.0865 | 34 | 0.2579 | | 0.0891 | 35 | 0.3312 | | 0.0916 | 36 | 0.4035 | | 0.0941 | 37 | 0.305 | | 0.0967 | 38 | 0.2898 | | 0.0992 | 39 | 0.2899 | | 0.1018 | 40 | 0.2713 | | 0.1043 | 41 | 0.3017 | | 0.1069 | 42 | 0.2395 | | 0.1094 | 43 | 0.1548 | | 0.1120 | 44 | 0.2468 | | 0.1145 | 45 | 0.1876 | | 0.1170 | 46 | 0.2322 | | 0.1196 | 47 | 0.2823 | | 0.1221 | 48 | 0.2158 | | 0.1247 | 49 | 0.2679 | | 0.1272 | 50 | 0.273 | | 0.1298 | 51 | 0.2876 | | 0.1323 | 52 | 0.197 | | 0.1349 | 53 | 0.1282 | | 0.1374 | 54 | 0.3355 | | 0.1399 | 55 | 0.1941 | | 0.1425 | 56 | 0.1873 | | 0.1450 | 57 | 0.2288 | | 0.1476 | 58 | 0.2802 | | 0.1501 | 59 | 0.2087 | | 0.1527 | 60 | 0.2239 | | 0.1552 | 61 | 0.225 | | 0.1578 | 62 | 0.1582 | | 0.1603 | 63 | 0.1972 | | 0.1628 | 64 | 0.1632 | | 0.1654 | 65 | 0.2101 | | 0.1679 | 66 | 0.2084 | | 0.1705 | 67 | 0.1499 | | 0.1730 | 68 | 0.1467 | | 0.1756 | 69 | 0.1428 | | 0.1781 | 70 | 0.2298 | | 0.1807 | 71 | 0.1883 | | 0.1832 | 72 | 0.22 | | 0.1858 | 73 | 0.1988 | | 0.1883 | 74 | 0.2091 | | 0.1908 | 75 | 0.1948 | | 0.1934 | 76 | 0.1348 | | 0.1959 | 77 | 0.112 | | 0.1985 | 78 | 0.1474 | | 0.2010 | 79 | 0.1949 | | 0.2036 | 80 | 0.1664 | | 0.2061 | 81 | 0.1807 | | 0.2087 | 82 | 0.1403 | | 0.2112 | 83 | 0.1225 | | 0.2137 | 84 | 0.1919 | | 0.2163 | 85 | 0.1403 | | 0.2188 | 86 | 0.1402 | | 0.2214 | 87 | 0.0981 | | 0.2239 | 88 | 0.1214 | | 0.2265 | 89 | 0.1755 | | 0.2290 | 90 | 0.1509 | | 0.2316 | 91 | 0.1551 | | 0.2341 | 92 | 0.176 | | 0.2366 | 93 | 0.1648 | | 0.2392 | 94 | 0.1622 | | 0.2417 | 95 | 0.1372 | | 0.2443 | 96 | 0.1016 | | 0.2468 | 97 | 0.1134 | | 0.2494 | 98 | 0.1436 | | 0.2519 | 99 | 0.1478 | | 0.2545 | 100 | 0.2065 | | 0.2570 | 101 | 0.1901 | | 0.2595 | 102 | 0.1859 | | 0.2621 | 103 | 0.212 | | 0.2646 | 104 | 0.2179 | | 0.2672 | 105 | 0.2471 | | 0.2697 | 106 | 0.1769 | | 0.2723 | 107 | 0.1593 | | 0.2748 | 108 | 0.204 | | 0.2774 | 109 | 0.1496 | | 0.2799 | 110 | 0.1212 | | 0.2824 | 111 | 0.1282 | | 0.2850 | 112 | 0.1126 | | 0.2875 | 113 | 0.1254 | | 0.2901 | 114 | 0.1422 | | 0.2926 | 115 | 0.1266 | | 0.2952 | 116 | 0.1305 | | 0.2977 | 117 | 0.1283 | | 0.3003 | 118 | 0.0737 | | 0.3028 | 119 | 0.1237 | | 0.3053 | 120 | 0.1185 | | 0.3079 | 121 | 0.0891 | | 0.3104 | 122 | 0.2312 | | 0.3130 | 123 | 0.2384 | | 0.3155 | 124 | 0.155 | | 0.3181 | 125 | 0.1118 | | 0.3206 | 126 | 0.1575 | | 0.3232 | 127 | 0.2115 | | 0.3257 | 128 | 0.098 | | 0.3282 | 129 | 0.1811 | | 0.3308 | 130 | 0.1704 | | 0.3333 | 131 | 0.1494 | | 0.3359 | 132 | 0.1531 | | 0.3384 | 133 | 0.1032 | | 0.3410 | 134 | 0.1137 | | 0.3435 | 135 | 0.1271 | | 0.3461 | 136 | 0.1591 | | 0.3486 | 137 | 0.1586 | | 0.3511 | 138 | 0.1292 | | 0.3537 | 139 | 0.1115 | | 0.3562 | 140 | 0.1337 | | 0.3588 | 141 | 0.1298 | | 0.3613 | 142 | 0.1649 | | 0.3639 | 143 | 0.0855 | | 0.3664 | 144 | 0.1124 | | 0.3690 | 145 | 0.0764 | | 0.3715 | 146 | 0.1402 | | 0.3740 | 147 | 0.137 | | 0.3766 | 148 | 0.0736 | | 0.3791 | 149 | 0.0772 | | 0.3817 | 150 | 0.1689 | | 0.3842 | 151 | 0.1371 | | 0.3868 | 152 | 0.1195 | | 0.3893 | 153 | 0.1536 | | 0.3919 | 154 | 0.1421 | | 0.3944 | 155 | 0.1222 | | 0.3969 | 156 | 0.1121 | | 0.3995 | 157 | 0.0892 | | 0.4020 | 158 | 0.1516 | | 0.4046 | 159 | 0.1071 | | 0.4071 | 160 | 0.1593 | | 0.4097 | 161 | 0.1078 | | 0.4122 | 162 | 0.1112 | | 0.4148 | 163 | 0.2101 | | 0.4173 | 164 | 0.2096 | | 0.4198 | 165 | 0.1337 | | 0.4224 | 166 | 0.1501 | | 0.4249 | 167 | 0.0989 | | 0.4275 | 168 | 0.0992 | | 0.4300 | 169 | 0.0926 | | 0.4326 | 170 | 0.0692 | | 0.4351 | 171 | 0.1235 | | 0.4377 | 172 | 0.1029 | | 0.4402 | 173 | 0.1351 | | 0.4427 | 174 | 0.0899 | | 0.4453 | 175 | 0.0844 | | 0.4478 | 176 | 0.1167 | | 0.4504 | 177 | 0.1355 | | 0.4529 | 178 | 0.092 | | 0.4555 | 179 | 0.1005 | | 0.4580 | 180 | 0.0891 | | 0.4606 | 181 | 0.1396 | | 0.4631 | 182 | 0.1024 | | 0.4656 | 183 | 0.1325 | | 0.4682 | 184 | 0.1061 | | 0.4707 | 185 | 0.1657 | | 0.4733 | 186 | 0.1141 | | 0.4758 | 187 | 0.149 | | 0.4784 | 188 | 0.1125 | | 0.4809 | 189 | 0.1524 | | 0.4835 | 190 | 0.1129 | | 0.4860 | 191 | 0.1089 | | 0.4885 | 192 | 0.1333 | | 0.4911 | 193 | 0.1377 | | 0.4936 | 194 | 0.0547 | | 0.4962 | 195 | 0.1057 | | 0.4987 | 196 | 0.1321 | | 0.5013 | 197 | 0.0979 | | 0.5038 | 198 | 0.1706 | | 0.5064 | 199 | 0.1559 | | 0.5089 | 200 | 0.1111 | | 0.5115 | 201 | 0.1258 | | 0.5140 | 202 | 0.0816 | | 0.5165 | 203 | 0.1362 | | 0.5191 | 204 | 0.1604 | | 0.5216 | 205 | 0.1104 | | 0.5242 | 206 | 0.1494 | | 0.5267 | 207 | 0.1402 | | 0.5293 | 208 | 0.1282 | | 0.5318 | 209 | 0.1543 | | 0.5344 | 210 | 0.1576 | | 0.5369 | 211 | 0.2071 | | 0.5394 | 212 | 0.1248 | | 0.5420 | 213 | 0.1237 | | 0.5445 | 214 | 0.0592 | | 0.5471 | 215 | 0.1769 | | 0.5496 | 216 | 0.1118 | | 0.5522 | 217 | 0.1608 | | 0.5547 | 218 | 0.1192 | | 0.5573 | 219 | 0.0551 | | 0.5598 | 220 | 0.1401 | | 0.5623 | 221 | 0.2046 | | 0.5649 | 222 | 0.1273 | | 0.5674 | 223 | 0.1319 | | 0.5700 | 224 | 0.1518 | | 0.5725 | 225 | 0.0929 | | 0.5751 | 226 | 0.1262 | | 0.5776 | 227 | 0.1566 | | 0.5802 | 228 | 0.1128 | | 0.5827 | 229 | 0.1467 | | 0.5852 | 230 | 0.1513 | | 0.5878 | 231 | 0.1989 | | 0.5903 | 232 | 0.0594 | | 0.5929 | 233 | 0.0838 | | 0.5954 | 234 | 0.0711 | | 0.5980 | 235 | 0.0854 | | 0.6005 | 236 | 0.1775 | | 0.6031 | 237 | 0.118 | | 0.6056 | 238 | 0.1297 | | 0.6081 | 239 | 0.1092 | | 0.6107 | 240 | 0.1469 | | 0.6132 | 241 | 0.1203 | | 0.6158 | 242 | 0.0901 | | 0.6183 | 243 | 0.1179 | | 0.6209 | 244 | 0.0864 | | 0.6234 | 245 | 0.1277 | | 0.6260 | 246 | 0.1313 | | 0.6285 | 247 | 0.089 | | 0.6310 | 248 | 0.0727 | | 0.6336 | 249 | 0.0556 | | 0.6361 | 250 | 0.0782 | | 0.6387 | 251 | 0.0869 | | 0.6412 | 252 | 0.0988 | | 0.6438 | 253 | 0.0818 | | 0.6463 | 254 | 0.1013 | | 0.6489 | 255 | 0.096 | | 0.6514 | 256 | 0.0622 | | 0.6539 | 257 | 0.1561 | | 0.6565 | 258 | 0.1282 | | 0.6590 | 259 | 0.1087 | | 0.6616 | 260 | 0.1312 | | 0.6641 | 261 | 0.1343 | | 0.6667 | 262 | 0.0955 | | 0.6692 | 263 | 0.0844 | | 0.6718 | 264 | 0.1209 | | 0.6743 | 265 | 0.0858 | | 0.6768 | 266 | 0.0714 | | 0.6794 | 267 | 0.1431 | | 0.6819 | 268 | 0.0632 | | 0.6845 | 269 | 0.115 | | 0.6870 | 270 | 0.1115 | | 0.6896 | 271 | 0.1239 | | 0.6921 | 272 | 0.1206 | | 0.6947 | 273 | 0.1894 | | 0.6972 | 274 | 0.0755 | | 0.6997 | 275 | 0.0709 | | 0.7023 | 276 | 0.1304 | | 0.7048 | 277 | 0.1476 | | 0.7074 | 278 | 0.1497 | | 0.7099 | 279 | 0.113 | | 0.7125 | 280 | 0.1676 | | 0.7150 | 281 | 0.0999 | | 0.7176 | 282 | 0.2044 | | 0.7201 | 283 | 0.1125 | | 0.7226 | 284 | 0.0956 | | 0.7252 | 285 | 0.0956 | | 0.7277 | 286 | 0.0771 | | 0.7303 | 287 | 0.0712 | | 0.7328 | 288 | 0.0525 | | 0.7354 | 289 | 0.0689 | | 0.7379 | 290 | 0.0964 | | 0.7405 | 291 | 0.1068 | | 0.7430 | 292 | 0.0536 | | 0.7455 | 293 | 0.0861 | | 0.7481 | 294 | 0.0813 | | 0.7506 | 295 | 0.0885 | | 0.7532 | 296 | 0.1083 | | 0.7557 | 297 | 0.1124 | | 0.7583 | 298 | 0.1095 | | 0.7608 | 299 | 0.08 | | 0.7634 | 300 | 0.1081 | | 0.7659 | 301 | 0.0719 | | 0.7684 | 302 | 0.0933 | | 0.7710 | 303 | 0.1143 | | 0.7735 | 304 | 0.065 | | 0.7761 | 305 | 0.1276 | | 0.7786 | 306 | 0.102 | | 0.7812 | 307 | 0.186 | | 0.7837 | 308 | 0.0778 | | 0.7863 | 309 | 0.1419 | | 0.7888 | 310 | 0.0895 | | 0.7913 | 311 | 0.1154 | | 0.7939 | 312 | 0.1037 | | 0.7964 | 313 | 0.0711 | | 0.7990 | 314 | 0.1559 | | 0.8015 | 315 | 0.0755 | | 0.8041 | 316 | 0.0799 | | 0.8066 | 317 | 0.1137 | | 0.8092 | 318 | 0.0837 | | 0.8117 | 319 | 0.1052 | | 0.8142 | 320 | 0.0846 | | 0.8168 | 321 | 0.0715 | | 0.8193 | 322 | 0.0923 | | 0.8219 | 323 | 0.1397 | | 0.8244 | 324 | 0.0899 | | 0.8270 | 325 | 0.1414 | | 0.8295 | 326 | 0.0422 | | 0.8321 | 327 | 0.0748 | | 0.8346 | 328 | 0.0739 | | 0.8372 | 329 | 0.0855 | | 0.8397 | 330 | 0.071 | | 0.8422 | 331 | 0.0557 | | 0.8448 | 332 | 0.1055 | | 0.8473 | 333 | 0.096 | | 0.8499 | 334 | 0.1083 | | 0.8524 | 335 | 0.133 | | 0.8550 | 336 | 0.1308 | | 0.8575 | 337 | 0.0661 | | 0.8601 | 338 | 0.0974 | | 0.8626 | 339 | 0.1027 | | 0.8651 | 340 | 0.1068 | | 0.8677 | 341 | 0.1653 | | 0.8702 | 342 | 0.097 | | 0.8728 | 343 | 0.0845 | | 0.8753 | 344 | 0.0546 | | 0.8779 | 345 | 0.1273 | | 0.8804 | 346 | 0.0982 | | 0.8830 | 347 | 0.0893 | | 0.8855 | 348 | 0.1222 | | 0.8880 | 349 | 0.1072 | | 0.8906 | 350 | 0.1254 | | 0.8931 | 351 | 0.0679 | | 0.8957 | 352 | 0.0995 | | 0.8982 | 353 | 0.0878 | | 0.9008 | 354 | 0.0564 | | 0.9033 | 355 | 0.113 | | 0.9059 | 356 | 0.0567 | | 0.9084 | 357 | 0.0968 | | 0.9109 | 358 | 0.1023 | | 0.9135 | 359 | 0.1106 | | 0.9160 | 360 | 0.091 | | 0.9186 | 361 | 0.0988 | | 0.9211 | 362 | 0.1374 | | 0.9237 | 363 | 0.0855 | | 0.9262 | 364 | 0.0824 | | 0.9288 | 365 | 0.058 | | 0.9313 | 366 | 0.0776 | | 0.9338 | 367 | 0.1195 | | 0.9364 | 368 | 0.0506 | | 0.9389 | 369 | 0.0893 | | 0.9415 | 370 | 0.1145 | | 0.9440 | 371 | 0.0695 | | 0.9466 | 372 | 0.0805 | | 0.9491 | 373 | 0.0824 | | 0.9517 | 374 | 0.0841 | | 0.9542 | 375 | 0.0919 | | 0.9567 | 376 | 0.064 | | 0.9593 | 377 | 0.2194 | | 0.9618 | 378 | 0.1165 | | 0.9644 | 379 | 0.0888 | | 0.9669 | 380 | 0.0826 | | 0.9695 | 381 | 0.0687 | | 0.9720 | 382 | 0.0933 | | 0.9746 | 383 | 0.1337 | | 0.9771 | 384 | 0.0738 | | 0.9796 | 385 | 0.0749 | | 0.9822 | 386 | 0.0742 | | 0.9847 | 387 | 0.1111 | | 0.9873 | 388 | 0.093 | | 0.9898 | 389 | 0.0877 | | 0.9924 | 390 | 0.0637 | | 0.9949 | 391 | 0.0897 | | 0.9975 | 392 | 0.0818 | | 1.0 | 393 | 0.0362 | | 1.0025 | 394 | 0.0561 | | 1.0051 | 395 | 0.0847 | | 1.0076 | 396 | 0.0752 | | 1.0102 | 397 | 0.0951 | | 1.0127 | 398 | 0.1069 | | 1.0153 | 399 | 0.0553 | | 1.0178 | 400 | 0.0929 | | 1.0204 | 401 | 0.0876 | | 1.0229 | 402 | 0.0381 | | 1.0254 | 403 | 0.1074 | | 1.0280 | 404 | 0.0763 | | 1.0305 | 405 | 0.0881 | | 1.0331 | 406 | 0.0481 | | 1.0356 | 407 | 0.1398 | | 1.0382 | 408 | 0.09 | | 1.0407 | 409 | 0.1045 | | 1.0433 | 410 | 0.088 | | 1.0458 | 411 | 0.0751 | | 1.0483 | 412 | 0.0781 | | 1.0509 | 413 | 0.0844 | | 1.0534 | 414 | 0.0949 | | 1.0560 | 415 | 0.0467 | | 1.0585 | 416 | 0.1159 | | 1.0611 | 417 | 0.0511 | | 1.0636 | 418 | 0.0659 | | 1.0662 | 419 | 0.043 | | 1.0687 | 420 | 0.0468 | | 1.0712 | 421 | 0.068 | | 1.0738 | 422 | 0.1022 | | 1.0763 | 423 | 0.1096 | | 1.0789 | 424 | 0.1113 | | 1.0814 | 425 | 0.1219 | | 1.0840 | 426 | 0.0852 | | 1.0865 | 427 | 0.0413 | | 1.0891 | 428 | 0.0797 | | 1.0916 | 429 | 0.1048 | | 1.0941 | 430 | 0.0494 | | 1.0967 | 431 | 0.079 | | 1.0992 | 432 | 0.0698 | | 1.1018 | 433 | 0.0908 | | 1.1043 | 434 | 0.0993 | | 1.1069 | 435 | 0.0397 | | 1.1094 | 436 | 0.0312 | | 1.1120 | 437 | 0.089 | | 1.1145 | 438 | 0.0318 | | 1.1170 | 439 | 0.0356 | | 1.1196 | 440 | 0.0588 | | 1.1221 | 441 | 0.0311 | | 1.1247 | 442 | 0.0578 | | 1.1272 | 443 | 0.1313 | | 1.1298 | 444 | 0.0897 | | 1.1323 | 445 | 0.0798 | | 1.1349 | 446 | 0.0326 | | 1.1374 | 447 | 0.143 | | 1.1399 | 448 | 0.0661 | | 1.1425 | 449 | 0.0433 | | 1.1450 | 450 | 0.0782 | | 1.1476 | 451 | 0.08 | | 1.1501 | 452 | 0.0505 | | 1.1527 | 453 | 0.0542 | | 1.1552 | 454 | 0.0755 | | 1.1578 | 455 | 0.0315 | | 1.1603 | 456 | 0.0667 | | 1.1628 | 457 | 0.0329 | | 1.1654 | 458 | 0.0791 | | 1.1679 | 459 | 0.0698 | | 1.1705 | 460 | 0.0194 | | 1.1730 | 461 | 0.0501 | | 1.1756 | 462 | 0.0449 | | 1.1781 | 463 | 0.0903 | | 1.1807 | 464 | 0.0503 | | 1.1832 | 465 | 0.0664 | | 1.1858 | 466 | 0.0457 | | 1.1883 | 467 | 0.0568 | | 1.1908 | 468 | 0.064 | | 1.1934 | 469 | 0.0253 | | 1.1959 | 470 | 0.046 | | 1.1985 | 471 | 0.0279 | | 1.2010 | 472 | 0.0733 | | 1.2036 | 473 | 0.0463 | | 1.2061 | 474 | 0.07 | | 1.2087 | 475 | 0.0281 | | 1.2112 | 476 | 0.0373 | | 1.2137 | 477 | 0.0738 | | 1.2163 | 478 | 0.0412 | | 1.2188 | 479 | 0.0545 | | 1.2214 | 480 | 0.0247 | | 1.2239 | 481 | 0.0293 | | 1.2265 | 482 | 0.0845 | | 1.2290 | 483 | 0.055 | | 1.2316 | 484 | 0.072 | | 1.2341 | 485 | 0.0481 | | 1.2366 | 486 | 0.0443 | | 1.2392 | 487 | 0.0807 | | 1.2417 | 488 | 0.0421 | | 1.2443 | 489 | 0.0237 | | 1.2468 | 490 | 0.0189 | | 1.2494 | 491 | 0.0604 | | 1.2519 | 492 | 0.0428 | | 1.2545 | 493 | 0.061 | | 1.2570 | 494 | 0.0723 | | 1.2595 | 495 | 0.0539 | | 1.2621 | 496 | 0.0747 | | 1.2646 | 497 | 0.0917 | | 1.2672 | 498 | 0.1161 | | 1.2697 | 499 | 0.087 | | 1.2723 | 500 | 0.0616 | | 1.2748 | 501 | 0.0756 | | 1.2774 | 502 | 0.0674 | | 1.2799 | 503 | 0.04 | | 1.2824 | 504 | 0.0354 | | 1.2850 | 505 | 0.0403 | | 1.2875 | 506 | 0.0596 | | 1.2901 | 507 | 0.0359 | | 1.2926 | 508 | 0.0648 | | 1.2952 | 509 | 0.0424 | | 1.2977 | 510 | 0.0605 | | 1.3003 | 511 | 0.0136 | | 1.3028 | 512 | 0.0547 | | 1.3053 | 513 | 0.0385 | | 1.3079 | 514 | 0.0191 | | 1.3104 | 515 | 0.1222 | | 1.3130 | 516 | 0.0906 | | 1.3155 | 517 | 0.0603 | | 1.3181 | 518 | 0.0366 | | 1.3206 | 519 | 0.0416 | | 1.3232 | 520 | 0.0832 | | 1.3257 | 521 | 0.0355 | | 1.3282 | 522 | 0.0614 | | 1.3308 | 523 | 0.0539 | | 1.3333 | 524 | 0.0566 | | 1.3359 | 525 | 0.0727 | | 1.3384 | 526 | 0.0311 | | 1.3410 | 527 | 0.0254 | | 1.3435 | 528 | 0.0376 | | 1.3461 | 529 | 0.0652 | | 1.3486 | 530 | 0.0717 | | 1.3511 | 531 | 0.0521 | | 1.3537 | 532 | 0.0404 | | 1.3562 | 533 | 0.041 | | 1.3588 | 534 | 0.0435 | | 1.3613 | 535 | 0.0842 | | 1.3639 | 536 | 0.0203 | | 1.3664 | 537 | 0.072 | | 1.3690 | 538 | 0.0277 | | 1.3715 | 539 | 0.0575 | | 1.3740 | 540 | 0.0665 | | 1.3766 | 541 | 0.024 | | 1.3791 | 542 | 0.0202 | | 1.3817 | 543 | 0.052 | | 1.3842 | 544 | 0.0532 | | 1.3868 | 545 | 0.0623 | | 1.3893 | 546 | 0.0643 | | 1.3919 | 547 | 0.0694 | | 1.3944 | 548 | 0.0582 | | 1.3969 | 549 | 0.0411 | | 1.3995 | 550 | 0.0245 | | 1.4020 | 551 | 0.0714 | | 1.4046 | 552 | 0.0489 | | 1.4071 | 553 | 0.0696 | | 1.4097 | 554 | 0.0316 | | 1.4122 | 555 | 0.0554 | | 1.4148 | 556 | 0.097 | | 1.4173 | 557 | 0.0665 | | 1.4198 | 558 | 0.0578 | | 1.4224 | 559 | 0.0746 | | 1.4249 | 560 | 0.0347 | | 1.4275 | 561 | 0.0471 | | 1.4300 | 562 | 0.0237 | | 1.4326 | 563 | 0.0269 | | 1.4351 | 564 | 0.068 | | 1.4377 | 565 | 0.0362 | | 1.4402 | 566 | 0.059 | | 1.4427 | 567 | 0.0321 | | 1.4453 | 568 | 0.0469 | | 1.4478 | 569 | 0.0445 | | 1.4504 | 570 | 0.0804 | | 1.4529 | 571 | 0.0387 | | 1.4555 | 572 | 0.0358 | | 1.4580 | 573 | 0.0322 | | 1.4606 | 574 | 0.0673 | | 1.4631 | 575 | 0.0302 | | 1.4656 | 576 | 0.0612 | | 1.4682 | 577 | 0.0553 | | 1.4707 | 578 | 0.0998 | | 1.4733 | 579 | 0.0396 | | 1.4758 | 580 | 0.0764 | | 1.4784 | 581 | 0.0427 | | 1.4809 | 582 | 0.0785 | | 1.4835 | 583 | 0.0419 | | 1.4860 | 584 | 0.0584 | | 1.4885 | 585 | 0.0437 | | 1.4911 | 586 | 0.0561 | | 1.4936 | 587 | 0.0131 | | 1.4962 | 588 | 0.0472 | | 1.4987 | 589 | 0.0479 | | 1.5013 | 590 | 0.0477 | | 1.5038 | 591 | 0.0745 | | 1.5064 | 592 | 0.0918 | | 1.5089 | 593 | 0.041 | | 1.5115 | 594 | 0.0463 | | 1.5140 | 595 | 0.0227 | | 1.5165 | 596 | 0.0427 | | 1.5191 | 597 | 0.0754 | | 1.5216 | 598 | 0.0489 | | 1.5242 | 599 | 0.0765 | | 1.5267 | 600 | 0.0651 | | 1.5293 | 601 | 0.0544 | | 1.5318 | 602 | 0.0777 | | 1.5344 | 603 | 0.0638 | | 1.5369 | 604 | 0.1198 | | 1.5394 | 605 | 0.0882 | | 1.5420 | 606 | 0.0236 | | 1.5445 | 607 | 0.0202 | | 1.5471 | 608 | 0.0955 | | 1.5496 | 609 | 0.0366 | | 1.5522 | 610 | 0.1021 | | 1.5547 | 611 | 0.0669 | | 1.5573 | 612 | 0.0185 | | 1.5598 | 613 | 0.0575 | | 1.5623 | 614 | 0.1001 | | 1.5649 | 615 | 0.0664 | | 1.5674 | 616 | 0.0617 | | 1.5700 | 617 | 0.0661 | | 1.5725 | 618 | 0.0425 | | 1.5751 | 619 | 0.0445 | | 1.5776 | 620 | 0.0773 | | 1.5802 | 621 | 0.0504 | | 1.5827 | 622 | 0.0785 | | 1.5852 | 623 | 0.0802 | | 1.5878 | 624 | 0.0882 | | 1.5903 | 625 | 0.0125 | | 1.5929 | 626 | 0.0305 | | 1.5954 | 627 | 0.0275 | | 1.5980 | 628 | 0.0245 | | 1.6005 | 629 | 0.0897 | | 1.6031 | 630 | 0.0444 | | 1.6056 | 631 | 0.0589 | | 1.6081 | 632 | 0.0337 | | 1.6107 | 633 | 0.0889 | | 1.6132 | 634 | 0.0556 | | 1.6158 | 635 | 0.0426 | | 1.6183 | 636 | 0.046 | | 1.6209 | 637 | 0.0342 | | 1.6234 | 638 | 0.0573 | | 1.6260 | 639 | 0.0569 | | 1.6285 | 640 | 0.0248 | | 1.6310 | 641 | 0.0214 | | 1.6336 | 642 | 0.0147 | | 1.6361 | 643 | 0.0203 | | 1.6387 | 644 | 0.0366 | | 1.6412 | 645 | 0.0484 | | 1.6438 | 646 | 0.0301 | | 1.6463 | 647 | 0.0314 | | 1.6489 | 648 | 0.0369 | | 1.6514 | 649 | 0.0168 | | 1.6539 | 650 | 0.0645 | | 1.6565 | 651 | 0.0755 | | 1.6590 | 652 | 0.0448 | | 1.6616 | 653 | 0.0795 | | 1.6641 | 654 | 0.0673 | | 1.6667 | 655 | 0.0431 | | 1.6692 | 656 | 0.0265 | | 1.6718 | 657 | 0.0567 | | 1.6743 | 658 | 0.0235 | | 1.6768 | 659 | 0.034 | | 1.6794 | 660 | 0.0812 | | 1.6819 | 661 | 0.0157 | | 1.6845 | 662 | 0.0448 | | 1.6870 | 663 | 0.0488 | | 1.6896 | 664 | 0.0515 | | 1.6921 | 665 | 0.0531 | | 1.6947 | 666 | 0.1166 | | 1.6972 | 667 | 0.0264 | | 1.6997 | 668 | 0.0325 | | 1.7023 | 669 | 0.0784 | | 1.7048 | 670 | 0.0859 | | 1.7074 | 671 | 0.0981 | | 1.7099 | 672 | 0.0411 | | 1.7125 | 673 | 0.0915 | | 1.7150 | 674 | 0.0396 | | 1.7176 | 675 | 0.1381 | | 1.7201 | 676 | 0.0547 | | 1.7226 | 677 | 0.0436 | | 1.7252 | 678 | 0.0519 | | 1.7277 | 679 | 0.0305 | | 1.7303 | 680 | 0.0356 | | 1.7328 | 681 | 0.0173 | | 1.7354 | 682 | 0.0299 | | 1.7379 | 683 | 0.0424 | | 1.7405 | 684 | 0.038 | | 1.7430 | 685 | 0.0159 | | 1.7455 | 686 | 0.0273 | | 1.7481 | 687 | 0.0301 | | 1.7506 | 688 | 0.0315 | | 1.7532 | 689 | 0.0566 | | 1.7557 | 690 | 0.0478 | | 1.7583 | 691 | 0.0533 | | 1.7608 | 692 | 0.0248 | | 1.7634 | 693 | 0.0454 | | 1.7659 | 694 | 0.0252 | | 1.7684 | 695 | 0.0326 | | 1.7710 | 696 | 0.0501 | | 1.7735 | 697 | 0.0196 | | 1.7761 | 698 | 0.0487 | | 1.7786 | 699 | 0.0445 | | 1.7812 | 700 | 0.1264 | | 1.7837 | 701 | 0.0312 | | 1.7863 | 702 | 0.1022 | | 1.7888 | 703 | 0.0293 | | 1.7913 | 704 | 0.0671 | | 1.7939 | 705 | 0.051 | | 1.7964 | 706 | 0.0246 | | 1.7990 | 707 | 0.1115 | | 1.8015 | 708 | 0.0203 | | 1.8041 | 709 | 0.0359 | | 1.8066 | 710 | 0.0699 | | 1.8092 | 711 | 0.0435 | | 1.8117 | 712 | 0.0689 | | 1.8142 | 713 | 0.0359 | | 1.8168 | 714 | 0.0321 | | 1.8193 | 715 | 0.0439 | | 1.8219 | 716 | 0.0652 | | 1.8244 | 717 | 0.0494 | | 1.8270 | 718 | 0.0864 | | 1.8295 | 719 | 0.0119 | | 1.8321 | 720 | 0.0284 | | 1.8346 | 721 | 0.0344 | | 1.8372 | 722 | 0.0454 | | 1.8397 | 723 | 0.0267 | | 1.8422 | 724 | 0.0152 | | 1.8448 | 725 | 0.0512 | | 1.8473 | 726 | 0.0537 | | 1.8499 | 727 | 0.0873 | | 1.8524 | 728 | 0.0934 | | 1.8550 | 729 | 0.0583 | | 1.8575 | 730 | 0.0206 | | 1.8601 | 731 | 0.0308 | | 1.8626 | 732 | 0.0443 | | 1.8651 | 733 | 0.0435 | | 1.8677 | 734 | 0.1254 | | 1.8702 | 735 | 0.0525 | | 1.8728 | 736 | 0.039 | | 1.8753 | 737 | 0.0157 | | 1.8779 | 738 | 0.0621 | | 1.8804 | 739 | 0.0405 | | 1.8830 | 740 | 0.0369 | | 1.8855 | 741 | 0.0568 | | 1.8880 | 742 | 0.0451 | | 1.8906 | 743 | 0.0657 | | 1.8931 | 744 | 0.0304 | | 1.8957 | 745 | 0.047 | | 1.8982 | 746 | 0.0457 | | 1.9008 | 747 | 0.0239 | | 1.9033 | 748 | 0.0669 | | 1.9059 | 749 | 0.0252 | | 1.9084 | 750 | 0.061 | | 1.9109 | 751 | 0.0429 | | 1.9135 | 752 | 0.0611 | | 1.9160 | 753 | 0.0482 | | 1.9186 | 754 | 0.0381 | | 1.9211 | 755 | 0.0749 | | 1.9237 | 756 | 0.0481 | | 1.9262 | 757 | 0.0405 | | 1.9288 | 758 | 0.0248 | | 1.9313 | 759 | 0.0377 | | 1.9338 | 760 | 0.061 | | 1.9364 | 761 | 0.0203 | | 1.9389 | 762 | 0.0315 | | 1.9415 | 763 | 0.0534 | | 1.9440 | 764 | 0.0383 | | 1.9466 | 765 | 0.0431 | | 1.9491 | 766 | 0.0509 | | 1.9517 | 767 | 0.0361 | | 1.9542 | 768 | 0.054 | | 1.9567 | 769 | 0.0248 | | 1.9593 | 770 | 0.1599 | | 1.9618 | 771 | 0.0657 | | 1.9644 | 772 | 0.0373 | | 1.9669 | 773 | 0.0632 | | 1.9695 | 774 | 0.0385 | | 1.9720 | 775 | 0.0456 | | 1.9746 | 776 | 0.0857 | | 1.9771 | 777 | 0.0253 | | 1.9796 | 778 | 0.0378 | | 1.9822 | 779 | 0.0366 | | 1.9847 | 780 | 0.0646 | | 1.9873 | 781 | 0.062 | | 1.9898 | 782 | 0.0513 | | 1.9924 | 783 | 0.0291 | | 1.9949 | 784 | 0.0466 | | 1.9975 | 785 | 0.0345 | | 2.0 | 786 | 0.0108 | | 2.0025 | 787 | 0.0196 | | 2.0051 | 788 | 0.0402 | | 2.0076 | 789 | 0.034 | | 2.0102 | 790 | 0.0606 | | 2.0127 | 791 | 0.0677 | | 2.0153 | 792 | 0.0174 | | 2.0178 | 793 | 0.0548 | | 2.0204 | 794 | 0.0385 | | 2.0229 | 795 | 0.0146 | | 2.0254 | 796 | 0.0716 | | 2.0280 | 797 | 0.0304 | | 2.0305 | 798 | 0.0512 | | 2.0331 | 799 | 0.0158 | | 2.0356 | 800 | 0.0973 | | 2.0382 | 801 | 0.0394 | | 2.0407 | 802 | 0.0724 | | 2.0433 | 803 | 0.0518 | | 2.0458 | 804 | 0.0385 | | 2.0483 | 805 | 0.0464 | | 2.0509 | 806 | 0.0501 | | 2.0534 | 807 | 0.051 | | 2.0560 | 808 | 0.0232 | | 2.0585 | 809 | 0.0631 | | 2.0611 | 810 | 0.0192 | | 2.0636 | 811 | 0.0301 | | 2.0662 | 812 | 0.0177 | | 2.0687 | 813 | 0.0172 | | 2.0712 | 814 | 0.0313 | | 2.0738 | 815 | 0.0653 | | 2.0763 | 816 | 0.0715 | | 2.0789 | 817 | 0.0548 | | 2.0814 | 818 | 0.0729 | | 2.0840 | 819 | 0.0399 | | 2.0865 | 820 | 0.0208 | | 2.0891 | 821 | 0.0476 | | 2.0916 | 822 | 0.054 | | 2.0941 | 823 | 0.0174 | | 2.0967 | 824 | 0.0431 | | 2.0992 | 825 | 0.0361 | | 2.1018 | 826 | 0.0514 | | 2.1043 | 827 | 0.0513 | | 2.1069 | 828 | 0.0099 | | 2.1094 | 829 | 0.0137 | | 2.1120 | 830 | 0.0493 | | 2.1145 | 831 | 0.0133 | | 2.1170 | 832 | 0.0087 | | 2.1196 | 833 | 0.0306 | | 2.1221 | 834 | 0.0092 | | 2.1247 | 835 | 0.0242 | | 2.1272 | 836 | 0.0905 | | 2.1298 | 837 | 0.0544 | | 2.1323 | 838 | 0.0462 | | 2.1349 | 839 | 0.0107 | | 2.1374 | 840 | 0.0846 | | 2.1399 | 841 | 0.031 | | 2.1425 | 842 | 0.027 | | 2.1450 | 843 | 0.05 | | 2.1476 | 844 | 0.0468 | | 2.1501 | 845 | 0.0251 | | 2.1527 | 846 | 0.031 | | 2.1552 | 847 | 0.0343 | | 2.1578 | 848 | 0.0149 | | 2.1603 | 849 | 0.0347 | | 2.1628 | 850 | 0.014 | | 2.1654 | 851 | 0.0471 | | 2.1679 | 852 | 0.0413 | | 2.1705 | 853 | 0.0047 | | 2.1730 | 854 | 0.0232 | | 2.1756 | 855 | 0.025 | | 2.1781 | 856 | 0.0621 | | 2.1807 | 857 | 0.0198 | | 2.1832 | 858 | 0.0346 | | 2.1858 | 859 | 0.0177 | | 2.1883 | 860 | 0.0298 | | 2.1908 | 861 | 0.0325 | | 2.1934 | 862 | 0.0075 | | 2.1959 | 863 | 0.0224 | | 2.1985 | 864 | 0.0085 | | 2.2010 | 865 | 0.0498 | | 2.2036 | 866 | 0.0222 | | 2.2061 | 867 | 0.0309 | | 2.2087 | 868 | 0.0074 | | 2.2112 | 869 | 0.0126 | | 2.2137 | 870 | 0.0372 | | 2.2163 | 871 | 0.0232 | | 2.2188 | 872 | 0.033 | | 2.2214 | 873 | 0.0111 | | 2.2239 | 874 | 0.0121 | | 2.2265 | 875 | 0.0552 | | 2.2290 | 876 | 0.0305 | | 2.2316 | 877 | 0.042 | | 2.2341 | 878 | 0.0147 | | 2.2366 | 879 | 0.0222 | | 2.2392 | 880 | 0.0341 | | 2.2417 | 881 | 0.0163 | | 2.2443 | 882 | 0.0084 | | 2.2468 | 883 | 0.0081 | | 2.2494 | 884 | 0.0312 | | 2.2519 | 885 | 0.0153 | | 2.2545 | 886 | 0.0262 | | 2.2570 | 887 | 0.0404 | | 2.2595 | 888 | 0.0198 | | 2.2621 | 889 | 0.0304 | | 2.2646 | 890 | 0.0544 | | 2.2672 | 891 | 0.065 | | 2.2697 | 892 | 0.0473 | | 2.2723 | 893 | 0.0291 | | 2.2748 | 894 | 0.0415 | | 2.2774 | 895 | 0.0398 | | 2.2799 | 896 | 0.018 | | 2.2824 | 897 | 0.0158 | | 2.2850 | 898 | 0.0161 | | 2.2875 | 899 | 0.0347 | | 2.2901 | 900 | 0.0104 | | 2.2926 | 901 | 0.044 | | 2.2952 | 902 | 0.019 | | 2.2977 | 903 | 0.0416 | | 2.3003 | 904 | 0.0039 | | 2.3028 | 905 | 0.0246 | | 2.3053 | 906 | 0.0133 | | 2.3079 | 907 | 0.0053 | | 2.3104 | 908 | 0.0992 | | 2.3130 | 909 | 0.0569 | | 2.3155 | 910 | 0.0326 | | 2.3181 | 911 | 0.0189 | | 2.3206 | 912 | 0.0115 | | 2.3232 | 913 | 0.0417 | | 2.3257 | 914 | 0.0161 | | 2.3282 | 915 | 0.0308 | | 2.3308 | 916 | 0.0234 | | 2.3333 | 917 | 0.027 | | 2.3359 | 918 | 0.0391 | | 2.3384 | 919 | 0.0107 | | 2.3410 | 920 | 0.0092 | | 2.3435 | 921 | 0.016 | | 2.3461 | 922 | 0.0299 | | 2.3486 | 923 | 0.0493 | | 2.3511 | 924 | 0.025 | | 2.3537 | 925 | 0.0127 | | 2.3562 | 926 | 0.0131 | | 2.3588 | 927 | 0.0214 | | 2.3613 | 928 | 0.0538 | | 2.3639 | 929 | 0.0082 | | 2.3664 | 930 | 0.043 | | 2.3690 | 931 | 0.0074 | | 2.3715 | 932 | 0.042 | | 2.3740 | 933 | 0.044 | | 2.3766 | 934 | 0.01 | | 2.3791 | 935 | 0.0055 | | 2.3817 | 936 | 0.0215 | | 2.3842 | 937 | 0.0258 | | 2.3868 | 938 | 0.0302 | | 2.3893 | 939 | 0.0326 | | 2.3919 | 940 | 0.0348 | | 2.3944 | 941 | 0.0444 | | 2.3969 | 942 | 0.019 | | 2.3995 | 943 | 0.0098 | | 2.4020 | 944 | 0.0283 | | 2.4046 | 945 | 0.0306 | | 2.4071 | 946 | 0.0316 | | 2.4097 | 947 | 0.01 | | 2.4122 | 948 | 0.0253 | | 2.4148 | 949 | 0.0664 | | 2.4173 | 950 | 0.0366 | | 2.4198 | 951 | 0.0307 | | 2.4224 | 952 | 0.0422 | | 2.4249 | 953 | 0.0133 | | 2.4275 | 954 | 0.0209 | | 2.4300 | 955 | 0.0065 | | 2.4326 | 956 | 0.0107 | | 2.4351 | 957 | 0.0396 | | 2.4377 | 958 | 0.0137 | | 2.4402 | 959 | 0.0258 | | 2.4427 | 960 | 0.0138 | | 2.4453 | 961 | 0.0275 | | 2.4478 | 962 | 0.0208 | | 2.4504 | 963 | 0.0302 | | 2.4529 | 964 | 0.0292 | | 2.4555 | 965 | 0.018 | | 2.4580 | 966 | 0.0168 | | 2.4606 | 967 | 0.0365 | | 2.4631 | 968 | 0.0141 | | 2.4656 | 969 | 0.0348 | | 2.4682 | 970 | 0.022 | | 2.4707 | 971 | 0.0677 | | 2.4733 | 972 | 0.0156 | | 2.4758 | 973 | 0.0424 | | 2.4784 | 974 | 0.0188 | | 2.4809 | 975 | 0.0494 | | 2.4835 | 976 | 0.0192 | | 2.4860 | 977 | 0.0346 | | 2.4885 | 978 | 0.0167 | | 2.4911 | 979 | 0.0274 | | 2.4936 | 980 | 0.0046 | | 2.4962 | 981 | 0.0301 | | 2.4987 | 982 | 0.0246 | | 2.5013 | 983 | 0.0222 | | 2.5038 | 984 | 0.0346 | | 2.5064 | 985 | 0.0595 | | 2.5089 | 986 | 0.0221 | | 2.5115 | 987 | 0.0211 | | 2.5140 | 988 | 0.0092 | | 2.5165 | 989 | 0.0225 | | 2.5191 | 990 | 0.0452 | | 2.5216 | 991 | 0.0288 | | 2.5242 | 992 | 0.044 | | 2.5267 | 993 | 0.0308 | | 2.5293 | 994 | 0.0309 | | 2.5318 | 995 | 0.0495 | | 2.5344 | 996 | 0.0384 | | 2.5369 | 997 | 0.0834 | | 2.5394 | 998 | 0.0866 | | 2.5420 | 999 | 0.0076 | | 2.5445 | 1000 | 0.0071 | | 2.5471 | 1001 | 0.0634 | | 2.5496 | 1002 | 0.0144 | | 2.5522 | 1003 | 0.077 | | 2.5547 | 1004 | 0.0347 | | 2.5573 | 1005 | 0.0081 | | 2.5598 | 1006 | 0.0216 | | 2.5623 | 1007 | 0.0437 | | 2.5649 | 1008 | 0.0367 | | 2.5674 | 1009 | 0.0281 | | 2.5700 | 1010 | 0.0312 | | 2.5725 | 1011 | 0.0181 | | 2.5751 | 1012 | 0.0226 | | 2.5776 | 1013 | 0.0558 | | 2.5802 | 1014 | 0.0267 | | 2.5827 | 1015 | 0.0596 | | 2.5852 | 1016 | 0.046 | | 2.5878 | 1017 | 0.0465 | | 2.5903 | 1018 | 0.0035 | | 2.5929 | 1019 | 0.019 | | 2.5954 | 1020 | 0.0118 | | 2.5980 | 1021 | 0.0128 | | 2.6005 | 1022 | 0.0458 | | 2.6031 | 1023 | 0.0185 | | 2.6056 | 1024 | 0.0309 | | 2.6081 | 1025 | 0.0142 | | 2.6107 | 1026 | 0.0732 | | 2.6132 | 1027 | 0.0327 | | 2.6158 | 1028 | 0.0296 | | 2.6183 | 1029 | 0.0237 | | 2.6209 | 1030 | 0.0169 | | 2.6234 | 1031 | 0.0306 | | 2.6260 | 1032 | 0.0235 | | 2.6285 | 1033 | 0.009 | | 2.6310 | 1034 | 0.0118 | | 2.6336 | 1035 | 0.0067 | | 2.6361 | 1036 | 0.008 | | 2.6387 | 1037 | 0.0202 | | 2.6412 | 1038 | 0.0241 | | 2.6438 | 1039 | 0.0118 | | 2.6463 | 1040 | 0.0161 | | 2.6489 | 1041 | 0.0242 | | 2.6514 | 1042 | 0.0072 | | 2.6539 | 1043 | 0.037 | | 2.6565 | 1044 | 0.0362 | | 2.6590 | 1045 | 0.0213 | | 2.6616 | 1046 | 0.0458 | | 2.6641 | 1047 | 0.0358 | | 2.6667 | 1048 | 0.024 | | 2.6692 | 1049 | 0.0093 | | 2.6718 | 1050 | 0.0306 | | 2.6743 | 1051 | 0.0075 | | 2.6768 | 1052 | 0.0193 | | 2.6794 | 1053 | 0.048 | | 2.6819 | 1054 | 0.0058 | | 2.6845 | 1055 | 0.0233 | | 2.6870 | 1056 | 0.0264 | | 2.6896 | 1057 | 0.0276 | | 2.6921 | 1058 | 0.0346 | | 2.6947 | 1059 | 0.0854 | | 2.6972 | 1060 | 0.0119 | | 2.6997 | 1061 | 0.0174 | | 2.7023 | 1062 | 0.0514 | | 2.7048 | 1063 | 0.0628 | | 2.7074 | 1064 | 0.0721 | | 2.7099 | 1065 | 0.0246 | | 2.7125 | 1066 | 0.049 | | 2.7150 | 1067 | 0.0148 | | 2.7176 | 1068 | 0.1024 | | 2.7201 | 1069 | 0.0312 | | 2.7226 | 1070 | 0.029 | | 2.7252 | 1071 | 0.0352 | | 2.7277 | 1072 | 0.0131 | | 2.7303 | 1073 | 0.0195 | | 2.7328 | 1074 | 0.0064 | | 2.7354 | 1075 | 0.0169 | | 2.7379 | 1076 | 0.0232 | | 2.7405 | 1077 | 0.0216 | | 2.7430 | 1078 | 0.0058 | | 2.7455 | 1079 | 0.0089 | | 2.7481 | 1080 | 0.0143 | | 2.7506 | 1081 | 0.0168 | | 2.7532 | 1082 | 0.0331 | | 2.7557 | 1083 | 0.0255 | | 2.7583 | 1084 | 0.0312 | | 2.7608 | 1085 | 0.0125 | | 2.7634 | 1086 | 0.0228 | | 2.7659 | 1087 | 0.0083 | | 2.7684 | 1088 | 0.0141 | | 2.7710 | 1089 | 0.0189 | | 2.7735 | 1090 | 0.0109 | | 2.7761 | 1091 | 0.0195 | | 2.7786 | 1092 | 0.0169 | | 2.7812 | 1093 | 0.0937 | | 2.7837 | 1094 | 0.019 | | 2.7863 | 1095 | 0.0856 | | 2.7888 | 1096 | 0.0155 | | 2.7913 | 1097 | 0.0408 | | 2.7939 | 1098 | 0.0279 | | 2.7964 | 1099 | 0.008 | | 2.7990 | 1100 | 0.086 | | 2.8015 | 1101 | 0.0078 | | 2.8041 | 1102 | 0.0186 | | 2.8066 | 1103 | 0.0468 | | 2.8092 | 1104 | 0.0255 | | 2.8117 | 1105 | 0.0418 | | 2.8142 | 1106 | 0.0188 | | 2.8168 | 1107 | 0.0197 | | 2.8193 | 1108 | 0.023 | | 2.8219 | 1109 | 0.0421 | | 2.8244 | 1110 | 0.0301 | | 2.8270 | 1111 | 0.0627 | | 2.8295 | 1112 | 0.0052 | | 2.8321 | 1113 | 0.0163 | | 2.8346 | 1114 | 0.0209 | | 2.8372 | 1115 | 0.0277 | | 2.8397 | 1116 | 0.0211 | | 2.8422 | 1117 | 0.0066 | | 2.8448 | 1118 | 0.0263 | | 2.8473 | 1119 | 0.0408 | | 2.8499 | 1120 | 0.0516 | | 2.8524 | 1121 | 0.0748 | | 2.8550 | 1122 | 0.0309 | | 2.8575 | 1123 | 0.007 | | 2.8601 | 1124 | 0.014 | | 2.8626 | 1125 | 0.0284 | | 2.8651 | 1126 | 0.0165 | | 2.8677 | 1127 | 0.0975 | | 2.8702 | 1128 | 0.0354 | | 2.8728 | 1129 | 0.0235 | | 2.8753 | 1130 | 0.0074 | | 2.8779 | 1131 | 0.0386 | | 2.8804 | 1132 | 0.0173 | | 2.8830 | 1133 | 0.0211 | | 2.8855 | 1134 | 0.0305 | | 2.8880 | 1135 | 0.0219 | | 2.8906 | 1136 | 0.0454 | | 2.8931 | 1137 | 0.0176 | | 2.8957 | 1138 | 0.0261 | | 2.8982 | 1139 | 0.0274 | | 2.9008 | 1140 | 0.0131 | | 2.9033 | 1141 | 0.0485 | | 2.9059 | 1142 | 0.0129 | | 2.9084 | 1143 | 0.05 | | 2.9109 | 1144 | 0.0306 | | 2.9135 | 1145 | 0.0352 | | 2.9160 | 1146 | 0.0271 | | 2.9186 | 1147 | 0.0216 | | 2.9211 | 1148 | 0.0567 | | 2.9237 | 1149 | 0.0258 | | 2.9262 | 1150 | 0.0221 | | 2.9288 | 1151 | 0.0112 | | 2.9313 | 1152 | 0.0199 | | 2.9338 | 1153 | 0.0388 | | 2.9364 | 1154 | 0.0101 | | 2.9389 | 1155 | 0.0179 | | 2.9415 | 1156 | 0.0358 | | 2.9440 | 1157 | 0.0247 | | 2.9466 | 1158 | 0.031 | | 2.9491 | 1159 | 0.0367 | | 2.9517 | 1160 | 0.0198 | | 2.9542 | 1161 | 0.0346 | | 2.9567 | 1162 | 0.011 | | 2.9593 | 1163 | 0.139 | | 2.9618 | 1164 | 0.0555 | | 2.9644 | 1165 | 0.0228 | | 2.9669 | 1166 | 0.0377 | | 2.9695 | 1167 | 0.024 | | 2.9720 | 1168 | 0.0331 | | 2.9746 | 1169 | 0.0815 | | 2.9771 | 1170 | 0.0116 | | 2.9796 | 1171 | 0.0186 | | 2.9822 | 1172 | 0.0153 | | 2.9847 | 1173 | 0.0557 | | 2.9873 | 1174 | 0.0406 | | 2.9898 | 1175 | 0.0334 | | 2.9924 | 1176 | 0.0265 | | 2.9949 | 1177 | 0.0333 | | 2.9975 | 1178 | 0.0177 | | 3.0 | 1179 | 0.0028 | Framework Versions - Python: 3.11.10 - Sentence Transformers: 4.0.2 - PyLate: 1.1.7 - Transformers: 4.48.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 2.21.0 - Tokenizers: 0.21.0

Smallest vocabulary variant with only 16k-token, ideal for European languages(English/French). LightOnOCR-1B is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs. ⚡ Speed: 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR 💸 Efficiency: Processes 5.71 pages/s on a single H100 (~493k pages/day) for <$0.01 per 1,000 pages 🧠 End-to-End: Fully differentiable, no external OCR pipeline 🧾 Versatile: Handles tables, receipts, forms, multi-column layouts, and math notation 🌍 Compact variants: 32k and 16k vocab options for European languages LightOnOCR combines a Vision Transformer encoder(Pixtral-based) with a lightweight text decoder(Qwen3-based) distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages. | Model | ArXiv | Old Scans | Math | Tables | Multi-Column | Tiny Text | Base | Overall | | :----------------- | :---: | :-------: | :--: | :----: | :----------: | :-------: | :--: | :-----: | | LightOnOCR-1B-1025 (151k vocab) | 81.4 | 71.6 | 76.4 | 35.2 | 80.0 | 88.7 | 99.5 | 76.1 | | LightOnOCR-1B-32k (32k vocab) | 80.6 | 66.2 | 73.5 | 33.5 | 71.2 | 87.6 | 99.5 | 73.1 | | LightOnOCR-1B-16k (16k vocab) | 82.3 | 72.9 | 75.3 | 33.5 | 78.6 | 85.1 | 99.8 | 75.4 | [2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀 Render PDFs to PNG or JPEG at a target longest dimension of 1280–1300 px Maintain aspect ratio to preserve text geometry LightOnOCR is robust to moderate skew; heavy rotation correction is optional Use one image per page; batching supported by vLLM | Variant | Description | | :--------------------------------------------------------------------------------- | :-------------------------------------------- | | LightOnOCR-1B-1025 | Full multilingual model (default) | | LightOnOCR-1B-32k | Fastest pruned-vocabulary version (32k tokens) optimized for European languages | | LightOnOCR-1B-16k | Most compact variant with smallest vocabulary | Transformers integration is coming soon for training. LoRA fine-tuning Domain adaptation (receipts, scientific articles, forms, etc.) Multilingual fine-tuning with task-specific corpora Example fine-tuning configurations will be released alongside the dataset. Trained on a diverse large-scale PDF corpus covering: Scientific papers, books, receipts, invoices, tables, forms, and handwritten text Multiple languages (Latin alphabet dominant) Real and synthetic document scans The dataset will be released under an open license.

Model Overview The MonoQwen2-VL-v0.1 is a multimodal reranker finetuned with LoRA from Qwen2-VL-2B, optimized for asserting pointwise image-query relevance using the MonoT5 objective. That is, given a couple of image and query fed into the prompt of the VLM, the model is tasked to generate "True" if the image is relevant to the query and "False" otherwise. During inference, a relevancy score can then be obtained by comparing the logits of the two tokens and this score can effectively be used to rerank the candidates generated by a first-stage retriever (such as DSE or ColPali) or filter them using a threshold. The ColPali train set was used to train this model with negatives mined using DSE. How to Use the Model Below is a quick example to rerank a single image against a user query using this model: This example demonstrates how to use the model to assess the relevance of an image with respect to a query. It outputs the probability that the image is relevant ("True") or not relevant ("False"). Note: this example requires `peft` to be installed in your environment (`pip install peft`). If you don't want to use `peft`, you can use model.loadadapter on the original Qwen2-VL-2B model. The model has been evaluated on ViDoRe Benchmark, by retrieving 10 elements with MrLightdse-qwen2-2b-mrl-v1 and reranking them. The table below summarizes its `ndcg@5` scores: | Dataset | MrLightdse-qwen2-2b-mrl-v1 | MonoQwen2-VL-v0.1 reranking | |---------------------------------------------------|--------------------------|------------------------| | vidore/arxivqatestsubsampled | 85.6 | 89.0 | | vidore/docvqatestsubsampled | 57.1 | 59.7 | | vidore/infovqatestsubsampled | 88.1 | 93.2 | | vidore/tabfquadtestsubsampled | 93.1 | 96.0 | | vidore/shiftprojecttest | 82.0 | 93.0 | | vidore/syntheticDocQAartificialintelligencetest| 97.5 | 100.0 | | vidore/syntheticDocQAenergytest | 92.9 | 97.7 | | vidore/syntheticDocQAgovernmentreportstest | 96.0 | 98.0 | | vidore/syntheticDocQAhealthcareindustrytest | 96.4 | 99.3 | | vidore/tatdqatest | 69.4 | 79.0 | | Mean | 85.8 | 90.5 | This LoRA model is licensed under the Apache 2.0 license. Citation If you find the model useful, consider citing our work:

Pruned-vocabulary version (32k tokens) optimized for European languages, offering additional speedup with minimal accuracy loss. LightOnOCR-1B is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs. ⚡ Speed: 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR 💸 Efficiency: Processes 5.71 pages/s on a single H100 (~493k pages/day) for <$0.01 per 1,000 pages 🧠 End-to-End: Fully differentiable, no external OCR pipeline 🧾 Versatile: Handles tables, receipts, forms, multi-column layouts, and math notation 🌍 Compact variants: 32k and 16k vocab options for European languages LightOnOCR combines a Vision Transformer encoder(Pixtral-based) with a lightweight text decoder(Qwen3-based) distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages. | Model | ArXiv | Old Scans | Math | Tables | Multi-Column | Tiny Text | Base | Overall | | :----------------- | :---: | :-------: | :--: | :----: | :----------: | :-------: | :--: | :-----: | | LightOnOCR-1B-1025 (151k vocab) | 81.4 | 71.6 | 76.4 | 35.2 | 80.0 | 88.7 | 99.5 | 76.1 | | LightOnOCR-1B-32k (32k vocab) | 80.6 | 66.2 | 73.5 | 33.5 | 71.2 | 87.6 | 99.5 | 73.1 | | LightOnOCR-1B-16k (16k vocab) | 82.3 | 72.9 | 75.3 | 33.5 | 78.6 | 85.1 | 99.8 | 75.4 | [2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀 Render PDFs to PNG or JPEG at a target longest dimension of 1280–1300 px Maintain aspect ratio to preserve text geometry LightOnOCR is robust to moderate skew; heavy rotation correction is optional Use one image per page; batching supported by vLLM | Variant | Description | | :--------------------------------------------------------------------------------- | :-------------------------------------------- | | LightOnOCR-1B-1025 | Full multilingual model (default) | | LightOnOCR-1B-32k | Fastest pruned-vocabulary version (32k tokens) optimized for European languages | | LightOnOCR-1B-16k | Most compact variant with smallest vocabulary | Transformers integration is coming soon for training. LoRA Domain adaptation (receipts, scientific articles, forms, etc.) Multilingual fine-tuning with task-specific corpora Example fine-tuning configurations will be released alongside the dataset. Trained on a diverse large-scale PDF corpus covering: Scientific papers, books, receipts, invoices, tables, forms, and handwritten text Multiple languages (Latin alphabet dominant) Real and synthetic document scans The dataset will be released under an open license.