8  Mistral OCR (Single)

from mistralai import Mistral
import sys
import os
from pathlib import Path
sys.path.insert(1, str(Path.cwd().parent.parent)) 

from src.fs import write_text_file

from pathlib import Path
from mistralai import DocumentURLChunk, ImageURLChunk, TextChunk
from mistralai.models import OCRResponse
import json

client = Mistral(api_key=os.environ['MISTRAL_API_KEY'])

8.1 OCR PDF

def mistral_ocr_pdf(file_path: str):
    """OCR one PDF file"""
    
    pdf_file = Path(file_path)
    if not pdf_file.is_file():
        raise FileNotFoundError(f"The file '{pdf_file}' does not exist.")

    uploaded_file = client.files.upload(
        file={
            "file_name": pdf_file.stem,
            "content": pdf_file.read_bytes(),
        },
        purpose="ocr",
    )

    signed_url = client.files.get_signed_url(file_id=uploaded_file.id, expiry=1)

    pdf_response = client.ocr.process(
        document=DocumentURLChunk(document_url=signed_url.url),
        model="mistral-ocr-latest",
        include_image_base64=True,
    )
    
    return pdf_response

ocr_mistral7b = mistral_ocr_pdf("docs/mistral7b.pdf")

ocr_mistral7b

OCRResponse(pages=[OCRPageObject(index=0, markdown='# Mistral 7B \n\nAlbert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed\n\n![img-0.jpeg](img-0.jpeg)\n\n\n#### Abstract\n\nWe introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B - Instruct, that surpasses Llama 2 13B - chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. Code: https://github.com/mistralai/mistral-src Webpage: https://mistral.ai/news/announcing-mistral-7b/\n\n\n## 1 Introduction\n\nIn the rapidly evolving domain of Natural Language Processing (NLP), the race towards higher model performance often necessitates an escalation in model size. However, this scaling tends to increase computational costs and inference latency, thereby raising barriers to deployment in practical, real-world scenarios. In this context, the search for balanced models delivering both high-level performance and efficiency becomes critically essential. Our model, Mistral 7B, demonstrates that a carefully designed language model can deliver high performance while maintaining an efficient inference. Mistral 7B outperforms the previous best 13B model (Llama 2, [26]) across all tested benchmarks, and surpasses the best 34B model (LLaMa 34B, [25]) in mathematics and code generation. Furthermore, Mistral 7B approaches the coding performance of Code-Llama 7B [20], without sacrificing performance on non-code related benchmarks.\n\nMistral 7B leverages grouped-query attention (GQA) [1], and sliding window attention (SWA) [6, 3]. GQA significantly accelerates the inference speed, and also reduces the memory requirement during decoding, allowing for higher batch sizes hence higher throughput, a crucial factor for real-time applications. In addition, SWA is designed to handle longer sequences more effectively at a reduced computational cost, thereby alleviating a common limitation in LLMs. These attention mechanisms collectively contribute to the enhanced performance and efficiency of Mistral 7B.', images=[OCRImageObject(id='img-0.jpeg', top_left_x=425, top_left_y=600, bottom_right_x=1283, bottom_right_y=893, 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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=1, markdown='Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior performance, we present a chat model fine-tuned from Mistral 7B that significantly outperforms the Llama 2 13B - Chat model.\n\nMistral 7B takes a significant step in balancing the goals of getting high performance while keeping large language models efficient. Through our work, our aim is to help the community create more affordable, efficient, and high-performing language models that can be used in a wide range of real-world applications.\n\n# 2 Architectural details \n\n![img-1.jpeg](img-1.jpeg)\n\nFigure 1: Sliding Window Attention. The number of operations in vanilla attention is quadratic in the sequence length, and the memory increases linearly with the number of tokens. At inference time, this incurs higher latency and smaller throughput due to reduced cache availability. To alleviate this issue, we use sliding window attention: each token can attend to at most $W$ tokens from the previous layer (here, $W=3$ ). Note that tokens outside the sliding window still influence next word prediction. At each attention layer, information can move forward by $W$ tokens. Hence, after $k$ attention layers, information can move forward by up to $k \\times W$ tokens.\n\nMistral 7B is based on a transformer architecture [27]. The main parameters of the architecture are summarized in Table 1. Compared to Llama, it introduces a few changes that we summarize below.\nSliding Window Attention. SWA exploits the stacked layers of a transformer to attend information beyond the window size $W$. The hidden state in position $i$ of the layer $k, h_{i}$, attends to all hidden states from the previous layer with positions between $i-W$ and $i$. Recursively, $h_{i}$ can access tokens from the input layer at a distance of up to $W \\times k$ tokens, as illustrated in Figure 1. At the last layer, using a window size of $W=4096$, we have a theoretical attention span of approximately $131 K$ tokens. In practice, for a sequence length of 16 K and $W=4096$, changes made to FlashAttention [11] and xFormers [18] yield a 2x speed improvement over a vanilla attention baseline.\n\n| Parameter | Value |\n| :-- | --: |\n| dim | 4096 |\n| n_layers | 32 |\n| head_dim | 128 |\n| hidden_dim | 14336 |\n| n_heads | 32 |\n| n_kv_heads | 8 |\n| window_size | 4096 |\n| context_len | 8192 |\n| vocab_size | 32000 |\n\nTable 1: Model architecture.\n\nRolling Buffer Cache. A fixed attention span means that we can limit our cache size using a rolling buffer cache. The cache has a fixed size of $W$, and the keys and values for the timestep $i$ are stored in position $i \\bmod W$ of the cache. As a result, when the position $i$ is larger than $W$, past values in the cache are overwritten, and the size of the cache stops increasing. We provide an illustration in Figure 2 for $W=3$. On a sequence length of 32 k tokens, this reduces the cache memory usage by 8 x , without impacting the model quality.\n\n[^0]\n[^0]:    ${ }^{1}$ https://github.com/mistralai/mistral-src\n    ${ }^{2}$ https://github.com/skypilot-org/skypilot\n    ${ }^{3}$ https://huggingface.co/mistralai', images=[OCRImageObject(id='img-1.jpeg', top_left_x=294, top_left_y=638, bottom_right_x=1405, bottom_right_y=1064, 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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=2, markdown='![img-2.jpeg](img-2.jpeg)\n\nFigure 2: Rolling buffer cache. The cache has a fixed size of $W=4$. Keys and values for position $i$ are stored in position $i \\bmod W$ of the cache. When the position $i$ is larger than $W$, past values in the cache are overwritten. The hidden state corresponding to the latest generated tokens are colored in orange.\n\nPre-fill and Chunking. When generating a sequence, we need to predict tokens one-by-one, as each token is conditioned on the previous ones. However, the prompt is known in advance, and we can pre-fill the $(k, v)$ cache with the prompt. If the prompt is very large, we can chunk it into smaller pieces, and pre-fill the cache with each chunk. For this purpose, we can select the window size as our chunk size. For each chunk, we thus need to compute the attention over the cache and over the chunk. Figure 3 shows how the attention mask works over both the cache and the chunk.\n\n| the | The cat sat on the mat and saw the dog go to |  |  |  |  |  |  |  |  |  |  |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |\n| dog | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |\n| go | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 |\n| to | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |\n|  | Past |  |  |  |  |  |  |  |  |  |  |  |\n\nFigure 3: Pre-fill and chunking. During pre-fill of the cache, long sequences are chunked to limit memory usage. We process a sequence in three chunks, "The cat sat on", "the mat and saw", "the dog go to". The figure shows what happens for the third chunk ("the dog go to"): it attends itself using a causal mask (rightmost block), attends the cache using a sliding window (center block), and does not attend to past tokens as they are outside of the sliding window (left block).\n\n# 3 Results \n\nWe compare Mistral 7B to Llama, and re-run all benchmarks with our own evaluation pipeline for fair comparison. We measure performance on a wide variety of tasks categorized as follow:\n\n- Commonsense Reasoning (0-shot): Hellaswag [28], Winogrande [21], PIQA [4], SIQA [22], OpenbookQA [19], ARC-Easy, ARC-Challenge [9], CommonsenseQA [24]\n- World Knowledge (5-shot): NaturalQuestions [16], TriviaQA [15]\n- Reading Comprehension (0-shot): BoolQ [8], QuAC [7]\n- Math: GSM8K [10] (8-shot) with maj@8 and MATH [13] (4-shot) with maj@4\n- Code: Humaneval [5] (0-shot) and MBPP [2] (3-shot)\n- Popular aggregated results: MMLU [12] (5-shot), BBH [23] (3-shot), and AGI Eval [29] (3-5-shot, English multiple-choice questions only)\n\nDetailed results for Mistral 7B, Llama 2 7B/13B, and Code-Llama 7B are reported in Table 2. Figure 4 compares the performance of Mistral 7B with Llama 2 7B/13B, and Llama $134 B^{4}$ in different categories. Mistral 7B surpasses Llama 2 13B across all metrics, and outperforms Llama 1 34B on most benchmarks. In particular, Mistral 7B displays a superior performance in code, mathematics, and reasoning benchmarks.\n\n[^0]\n[^0]:    ${ }^{4}$ Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.', images=[OCRImageObject(id='img-2.jpeg', top_left_x=294, top_left_y=191, bottom_right_x=1405, bottom_right_y=380, 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')], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=3, markdown='![img-3.jpeg](img-3.jpeg)\n\nFigure 4: Performance of Mistral 7B and different Llama models on a wide range of benchmarks. All models were re-evaluated on all metrics with our evaluation pipeline for accurate comparison. Mistral 7B significantly outperforms Llama 2 7B and Llama 2 13B on all benchmarks. It is also vastly superior to Llama 1 34B in mathematics, code generation, and reasoning benchmarks.\n\n| Model | Modality | MMLU | HellaSwag | WinoG | PIQA | Arc-e | Arc-c | NQ | TriviaQA | HumanEval | MBPP | MATH | GSM8K |\n| :-- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n| LLaMA 2 7B | Pretrained | $44.4 \\%$ | $77.1 \\%$ | $69.5 \\%$ | $77.9 \\%$ | $68.7 \\%$ | $43.2 \\%$ | $24.7 \\%$ | $63.8 \\%$ | $11.6 \\%$ | $26.1 \\%$ | $3.9 \\%$ | $16.0 \\%$ |\n| LLaMA 2 13B | Pretrained | $55.6 \\%$ | $\\mathbf{8 0 . 7 \\%}$ | $72.9 \\%$ | $80.8 \\%$ | $75.2 \\%$ | $48.8 \\%$ | $\\mathbf{2 9 . 0 \\%}$ | $\\mathbf{6 9 . 6 \\%}$ | $18.9 \\%$ | $35.4 \\%$ | $6.0 \\%$ | $34.3 \\%$ |\n| Code-Llama 7B | Finetuned | $36.9 \\%$ | $62.9 \\%$ | $62.3 \\%$ | $72.8 \\%$ | $59.4 \\%$ | $34.5 \\%$ | $11.0 \\%$ | $34.9 \\%$ | $\\mathbf{3 1 . 1 \\%}$ | $\\mathbf{5 2 . 5 \\%}$ | $5.2 \\%$ | $20.8 \\%$ |\n| Mistral 7B | Pretrained | $\\mathbf{6 0 . 1 \\%}$ | $\\mathbf{8 1 . 3 \\%}$ | $\\mathbf{7 5 . 3 \\%}$ | $\\mathbf{8 3 . 0 \\%}$ | $\\mathbf{8 0 . 0 \\%}$ | $\\mathbf{5 5 . 5 \\%}$ | $\\mathbf{2 8 . 8 \\%}$ | $\\mathbf{6 9 . 9 \\%}$ | $\\mathbf{3 0 . 5 \\%}$ | $47.5 \\%$ | $\\mathbf{1 3 . 1 \\%}$ | $\\mathbf{5 2 . 2 \\%}$ |\n\nTable 2: Comparison of Mistral 7B with Llama. Mistral 7B outperforms Llama 2 13B on all metrics, and approaches the code performance of Code-Llama 7B without sacrificing performance on non-code benchmarks.\n\nSize and Efficiency. We computed "equivalent model sizes" of the Llama 2 family, aiming to understand Mistral 7B models\' efficiency in the cost-performance spectrum (see Figure 5). When evaluated on reasoning, comprehension, and STEM reasoning (specifically MMLU), Mistral 7B mirrored performance that one might expect from a Llama 2 model with more than 3x its size. On the Knowledge benchmarks, Mistral 7B\'s performance achieves a lower compression rate of 1.9 x , which is likely due to its limited parameter count that restricts the amount of knowledge it can store.\n\nEvaluation Differences. On some benchmarks, there are some differences between our evaluation protocol and the one reported in the Llama 2 paper: 1) on MBPP, we use the hand-verified subset 2) on TriviaQA, we do not provide Wikipedia contexts.\n\n## 4 Instruction Finetuning\n\nTo evaluate the generalization capabilities of Mistral 7B, we fine-tuned it on instruction datasets publicly available on the Hugging Face repository. No proprietary data or training tricks were utilized: Mistral 7B - Instruct model is a simple and preliminary demonstration that the base model can easily be fine-tuned to achieve good performance. In Table 3, we observe that the resulting model, Mistral 7B - Instruct, exhibits superior performance compared to all 7B models on MT-Bench, and is comparable to 13B - Chat models. An independent human evaluation was conducted on https://limboxing.com/leaderboard.\n\n| Model | Chatbot Arena <br> ELO Rating | MT Bench |\n| :-- | :--: | :--: |\n| WizardLM 13B v1.2 | 1047 | 7.2 |\n| Mistral 7B Instruct | $\\mathbf{1 0 3 1}$ | $\\mathbf{6 . 8 4}$ +/- $\\mathbf{0 . 0 7}$ |\n| Llama 2 13B Chat | 1012 | 6.65 |\n| Vicuna 13B | 1041 | 6.57 |\n| Llama 2 7B Chat | 985 | 6.27 |\n| Vicuna 7B | 997 | 6.17 |\n| Alpaca 13B | 914 | 4.53 |\n\nTable 3: Comparison of Chat models. Mistral 7B Instruct outperforms all 7B models on MT-Bench, and is comparable to 13B - Chat models.\n\nIn this evaluation, participants were provided with a set of questions along with anonymous responses from two models and were asked to select their preferred response, as illustrated in Figure 6. As of October 6, 2023, the outputs generated by Mistral 7B were preferred 5020 times, compared to 4143 times for Llama 2 13B.', images=[OCRImageObject(id='img-3.jpeg', top_left_x=292, top_left_y=204, bottom_right_x=1390, bottom_right_y=552, 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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=4, markdown='![img-4.jpeg](img-4.jpeg)\n\nFigure 5: Results on MMLU, commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 (7B/13B/70B). Mistral 7B largely outperforms Llama 2 13B on all evaluations, except on knowledge benchmarks, where it is on par (this is likely due to its limited parameter count, which limits the amount of knowledge it can compress).\n\n# 5 Adding guardrails for front-facing applications \n\nThe ability to enforce guardrails when it comes to AI generation is important for front-facing applications. In this section, we highlight how to leverage system prompting to optionally enforce output constraints on top of our models. Additionally, we showcase the ability of Mistral 7B to perform fine-grained content moderation, which can be useful to enforce quality content in applications.\n\n### 5.1 System prompt to enforce guardrails\n\nWe introduce a system prompt (see below) to guide the model to generate answers within specified guardrails, similar to the work done with Llama 2. Using this prompt allows the user to move on the Pareto front of model utility / guardrails enforcement, as indicated in Table 4.\n\nAlways assist with care, respect, and truth. Respond with utmost utility yet securely. Avoid harmful, unethical, prejudiced, or negative content. Ensure replies promote fairness and positivity.\n\nWe use a set of 175 unsafe prompts for evaluating safety. With the recommended system prompt, the model properly declines to answer $100 \\%$ of the harmful questions.\nAs an illustration, we provide in Table 5 the answers of both Mistral 7B - Instruct and Llama 2 Chat 13B to the question How to kill a linux process with system prompts activated. We observe that Mistral 7B provides a correct response while Llama 2 declines to answer. Note that on this specific question, both models answer correctly when system prompts are deactivated.\n\n| Guardrails | MT Bench |\n| :-- | :--: |\n| No system prompt | $6.84 \\pm 0.07$ |\n| Llama 2 system prompt | $6.38 \\pm 0.07$ |\n| Mistral system prompt | $6.58 \\pm 0.05$ |\n\nTable 4: System prompts. Mean official MT Bench score over 10 iterations with standard deviation for Mistral 7B - Instruct. For reference, Llama 2 13B - Chat reports official results of 6.65 .\n\n### 5.2 Content moderation with self-reflection\n\nMistral 7B - Instruct can be used as a content moderator: the model itself is able to accurately classify a user prompt or its generated answer as being either acceptable or falling into one of the following categories: Illegal activities such as terrorism, child abuse or fraud; Hateful, harassing or violent content such as discrimination, self-harm or bullying; Unqualified advice for instance in legal, medical or financial domains.', images=[OCRImageObject(id='img-4.jpeg', top_left_x=464, top_left_y=202, bottom_right_x=1232, bottom_right_y=734, 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n5hwfzxn8a0KACiiigAooooAKKKKACiiigAooooAKKKKACiiigArG8TaRNrWiSWttKsV0rpNBIwyFkRgy59sitmigDhZ9R8cahbSad/wi9rZyyoYnvmv1eJQerKgG49TgHFddpdgumaXa2KEstvGsYY98CrlFAHjOk3uuad4V1/xQ39muttqN1Jc288G55wshDAvn5CAAFUDHA65rqNNjt9S8c+K5LyFX22dosCzKDtiZHJwD0y2c/QVtXfgbQr6/lu57WUmeUTTwLcSLBM46M8QYIxyByRzjmptX8I6Trd0tzdwzrOIjA0lvcyQmSMnOx9hG5evB9TQBl+BZbxvhxprQBJJBG4j81iB5YdgvI5+6BW54b3f8I7ZbgA3l8gdOpq9BbRWdmltbxrFBEmxEUcKAMAD2qn4d/wCQBZ/7n9TQBqUUlG4CgBjyrEhd2CqoyxPQCufs0bXr8X84P2GFv9GjPRz/AHz/AEpb2R9d1FtOgYiyhObqQfxn/nmP61vRxpFGERQqqMBR0AqdzX+HHzf4DwvHSlxS0VRkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ8j241yFDETcGBir5425AIq/kVQkW2/tuF2kcXQgYKv8JXIyfrnFYui2xlxM2mmRhcyf6QZ+mJGx8ue1AHU5GetLmuMmtrtrS+nM1sXkuvKdvs7biNwGM7/u+1XZNSuLOSWztEhQWqgbY7KRlkbbnA28IO3JJoA6bIpMj1rGS9vr+7kitfKthFGjSefEXbcwyFxkYAH41FEb06tqm+4ja3jCHyjGehXPB3ce/HPtQBvbhjOeKNwHesG21Vra0t/NjjjgayWWMJnqAMrkn3GO9RyaverJHbjZDKIBJIRbSSgE9FwvTHqaAOjpMiq1ncSXWnxTyxNC7pkowIIP0NZ+kOzeELZ2ZixtMkk852560AbWaMisqwuPJ8NWk8kiri1jYu4z/COTWf8A2pcywX1tMS3+ivLG/wBneDtjADEn8aAOkyPWjIrh7qac6BLpYkkDwxNMz7jkxhQV5+pUf8BNXtevI5JLexa9S1aOBrncz7cuBiMH2zkn6UAdXmiqmn3i6hp9vdJx5qBiP7p7j8DVugAooooAKKKKACiiigAooooAKRmVVLMQABkk9qWqeo6fFqmmXVhcbxFcxNE5RtpAIwcHtQBxVj4n1XUviDpqxSBNBvre5+zxFBulEWwecTjIDFyAM9AD3qoniC8XxJqEereItQ06CPUmht4/sCG3aPC7QZSh5JyPvVfHw8Nt4r0PULXUb5rPT4pVKSzglc+XtVePunaQR6Yqz4i07xN4itbrRmtbG3sZ5CpuvNLMIt3BC4+9t/WgDb8S3tta6JOJ50jMo2puONxz0Falv/x7Rf7g/lWfrEfleH7lASQsYAJPJxjr/jWhB/x7Rf7g/lQBJuA70jyIilnYKo6k9q8n8XavqJ8QXEJnlijiYCNVYgY9a6Pw1ZT6/pUV1qtxNLGp2pETtVgOhOOtZqpeTjY7J4RwpKpKWjNqfxCkkrQaXA99OOpThF+rdKjGjXupndrF0fK6/ZbclU/4Eepragto7aNY4Y1RF6KowBU31qrN7nP7RLSCK9taQ2kCw28KRRjoqDAqwBx0paKozeoUmKWigAooooAzZ5ZhrtrEGbymhkLAfdyMYzTRpUyXU88F/LEJpA7IEUjPTvn0qSW5lTWrW2BHlyROzcc5GKqx6hcf2o0M7LChkKIjRn5x7NQBal0yOeK+VpGxeKEcjt8u3iifTTJcJcwXDQzqnllgAwdeuCKzVvZo9JjltmjR2ZlSMLuLtnjFLLqd9HcR2bFFnSBZJnWMuMkkYGPoaAL8umyyNDMt263USlDIVBDqeoI4GOmPpTjp7faobkXUgmRNjnauJBnoeOKl0+4kurCKWaNopWHzIRgg1Q0+FbbXb+JGfy/KjbDMTyS3PP0oAkl0SGbTPsJmkUBmcOPvAnOf5mpJtMDeQ1rKYJIFMaMoBG04yCOhHA/Ks66hFpq1nJHE8MbTYM3mlg+RwpH1rNnEqw3915MimK8Y/ahL/q1EmT8vegDrreKSKILLMZnB5cgD+QqjBpclvNK0OoSLFJK0jR7FIyeTzip9WuXtdHu7iP78cLMv1Aqhq9kkXh04eTdbxZRg5HOOp9aAJ4dHkgRolv5fs7FsxbFxhs8Zx70lto/k3NvLJeSS/ZgyxoQAACPYcn3rUi/1SZ64Ga5OR7izktXkjuBeteqskhJ2MjHp1xjB/SgDoLPTLa0itlCb3twVjkYfMATzVe30Gzi8gSYuFt4fJRZVDAcjn68CoX1S5Gtf2Xtj8xnEgk7CLqR/vf8A66s27tFr95BkmN4o5wP7rZZT+e0UAWLSwisZJzACElYNsHAU9OKuUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFGaACiiigAooooAKKKKAGv9xvoazfDv/IAs/8Ac/qa0n+430NZvh7jQLP/AHP6mgDTLADk1h6vfzPOml6e3+mTLln7Qp/ePv6VyfxA1G/h1KG2SSSK38vcpU43Hvk+1Xvh3JNPBfSSgv8AOuJm5LcEYz6DAx9ay57y5TtWFcKPt2zr9PsItNs0toF+VepPVj3J9zVog56U6itTibu7sKKKKACiiigAooooAKKKKACiiigAooooAKKKKAM+SCM63DcGYB1hZRF3IJHNRw6PDbOWjuLlEDl/LEvy5JyePxp8tqW1yC63phIHTb/EckfpVC3vLpJWiv5HWd0crGYx5bYGflP+NAGkLC0kt2Rfmjll844bq3X+lJNpkctw06SzwyOMOYnxu7cj+tZRnupbGwSzuSlxNCh8tVG1Rjlj6CknvtRa6uobfzS1sFVQsYIc7c5Y5oA1J9Mheb7QJp4HCBHeOTbuUf3v8aUWVrNdvdJK28qEfZJw2MjkVYO2Wz/0hQqtH84Y8DjkVk6Zbxy6nJe2kCwWfleUoVdvmnOd2B29/c0AW5NJsbm2trdxvS1xs+bpjjn8qfcWdvd3AlSd45wNu6GTBI9CKp6ZBFa6nqsUKbEHl4Ue6k8Vj2cTRWumXLQQpH9owZY/vtkkAH25oA65UWCDYXbaq8s7ZOPc1m2+k2dnHHEl3OIguFief5cdOlSa6SbBI+Qks8UTkf3WdQfz6fjTdYsxPbO0dpazyhCpM3Ye3BoAWPRLZIPI866eDZ5YjaYlduOlKmkWiSSb5ZZZZYTExkk3Er3x6VlyapLY6Rp09s5mWSALscfN04c/Sr1wXS40qXzPNkZyjOOjArkmgC/Jp9vLaPbNH+7eIxHHXbjGM06Cyit5ppo1O+bbvJP90YH04qzRQBXtbSO0V0iXarSNIR2BJycfjzViiigAooooAKKKKACiiigAooooAKKKKACiiigDP1mKWfR7qKGMySMmFUEDcfxqtFq10kMaHQtSyFAP+p9P+ulbFFAHKarPZXMkEmoeGr5nZxGjMsOST0H+srSi1KWCJY4tA1JY1GFVRDgD/v5VLxDreq6StxcJoEN3YWkZna5kvljwFXJIUqTxzzmtTQtRk1jQrHU5bRrV7qFZvJdtxQMMgE/Sgbk2km9CGXXZoYzI+h6oFHXAiP8AKSnf2xcf9AHU/wDyD/8AHK1sUUCMd9dmjaNW0LVMyNtXiI5OCf8Anp6A0/8Ati5/6AWp/wDkH/45Wpjpx0paAMr+2Ln/AKAWp/8AkH/45R/bFz/0AtT/APIP/wAcrVooAyv7Yuf+gFqf/kH/AOOUf2zcf9ALU/8AyD/8crVrE8SazeaFpcl7aaRLqPlqzyqkyRiNFXcWJY89OgBNAEEniC4XWLe2/sG/y8Uj8iLdhSoOP3nT5hSGVmuVnbRtYZlbeqNLEVU+oXzKsw6lPfeG7TWrDTPPubi2jlitzKqEBwrY3ngADGeOcVX0bxHdX2s3Oj6lpbaffxQicBbgTI6E4yGAHOexFAETojeV/wASLWFMQYIVkjGM8nH7ynyM0hjf+xtZWWNNglWSIOR1wT5nNdJRQBjQ6nPDEsa6HqpCjALGEn8T5lVIj5N612miaz5zcEmSMjH08z3rpKKAObVmFws7aLrMpUllWSSNgp9h5n1qD9xNLOh0LWCGffJGXj2Fm55HmYNdXSYwelAGPNqUs8EkMmgak0brtYfueQeP+elUpHluNOis7rRtXkVF2kq0SbsDviWumooAwI7qSNIV/sPVyITlCXjz+P73mmCVvtQuG0TWHcNuVXeMqp9h5ldFRQBzpdTnPh7VNxl87fmLdu+vmenGPShNSltbq4upNF1RnndEyFiOAOFH+s9SfzroqQjigDL/ALZuP+gFqf8A5B/+OVFP4ha2VGm0XUkDsEXIi5J7f6ytC/mubeykls7P7XcAfLEJAm//AIEelYmleIZ77W5NG1fSfsF8kK3MaicTo6ZIyCAMEEdxQBf/ALZuP+gFqf8A5B/+OUv9sXP/AEAtT/8AIP8A8crNj8ZW83jdfDcNtI/7uRmuww2CRApZB6kB1z6E46g109AGONdmaV4xoeqb0AJGIuhzj/lp7Gn/ANsXP/QC1P8A8g//ABytXHOcUUAZX9sXP/QC1P8A8g//ABymS67NDE8j6HqgVFLMQIjwOvAkrYoxxigDK/ti5/6AWp/+Qf8A45R/bFz/ANALU/8AyD/8crVooAx5NdmiUF9C1TBIAwIjyenSSn/2xc/9ALU//IP/AMcrUwSKWgDK/tm4/wCgFqf/AJB/+OVDD4ia48zytF1NvLco/EXDDqP9Z71meJPFWs+HLe8vn8Oxzafb4xOL9VZ8nAwmzPUjjNS33iO7try303TdFFzqc1ubueEzrEsI4By+DlieOnOKANT+2Lj/AKAWp/8AkH/45WlGxeNXKMpIyVbGR+VZ+g61Dr+kRahBG8YfKtG/3o3BwVP41qUAFFFFABRRRQAUUUUANYEqQPQ1z2lXt7Y6Xb2smh6iXjXaSvlYPJ6fvK6OkoA5rVr+2lsnn1Lw7fNBCpdmdYTtA6niSrNtemzhEVv4d1COMfwqIQP/AEZUmt3Wqw7IrDRItSikUiXzLpYtvtgqc96r+EfEUnifSJL97D7GouJYEHmiUSBG2lgwA4JDflSsiuaVrX0LTa1Oilm0LU8AZPEP/wAcpE1ud0V10LU8MMjiEf8AtStfFGKZJjy67NDGZH0LVAo64ER/lJT/AO2Ln/oBan/5B/8AjlauKKAMr+2Ln/oBan/5B/8AjlH9sXP/AEAtT/8AIP8A8crVooAyv7Yuf+gFqf8A5B/+OUf2xcf9ALU//IP/AMcrVqlqlzc2WnyT2WnyX9wuNlvG6IX5x1cgcZJ69qAKU3iF7cxiXRdTXzHCJxEcseg/1lS/2xcf9ALU/wDyD/8AHKqeHtdbxH4aTVxppjmDyqLVpVYh43ZCA3Tkqefeq1p4n1BNatNM1nQvsD3of7PJHdLOpZRkq2ACpx9RQBqf2xc/9ALU/wDyD/8AHKP7Yuf+gFqf/kH/AOOVq0UAZX9sXP8A0AtT/wDIP/xyj+2Ln/oBan/5B/8AjlatFAGV/bFz/wBALU//ACD/APHKYddmEojOh6puILAYi6D/ALaVsUmOen40Ac5Je3za5DdjQtS8pIGQ8w9SR/00p0Txwvvj8N6kGIxyYjjPXH7ziuiooA5txDIys3hnUCyqEBxFkKOg/wBZ0FPmkjnYPJ4b1EsBjI8oZHTn95zXQ0UAY0uoyTQPDJ4e1Jo2XaVIh5Hp/rKqwTxaeGkh8N6jGFUg7fKxj2HmV0dFAHN20kMEn2iDw1qCO4zuHlAnj3kpjXFpYI943hu/jSAGQuREdoHJI/eV0shZImZE3sASqDjJx0rlo/E1+ur2ema54f8AsS6jujgdLtJwzBclXAAxxn1FAF261CS+snhfRNUCSj737kEeh/1lMuJFu3D3Hh3U5GC7SSYuR7jzKg13xjbaHrul6Qlo9xNezxxOUYBbYOcIzfUhsD/ZNdRmgDA/tFVuFT/hHdR8zyiqjbFgIMZH+s9xQLl1mgkTQtTUQqRGg8nAzxn/AFlb2DnOKWgDK/ti5/6AWp/+Qf8A45R/bFz/ANALU/8AyD/8crVooAx4tdmmiSRND1Qq4DKSIhwenBkp/wDbFz/0AtT/APIP/wAcrVxxjtRQBkSa5NFE8j6FqgVAWJAiPA9hJThrNwR/yAtT/wDIP/xytXFFAGV/bNx/0AtT/wDIP/xyok8QvJcS26aLqZliVWdcRcBs4/5aexpmt6rrWnSs1lokV5apF5jzPfLDtIzkEFT2HX3rJg8ZtcaBpGpw6Kx1HWTstrMzAEgbiC0m3hduTnHegDd/tm4x/wAgLU//ACD/APHK0LaZp4Fka3kgY9Ul27h/3ySP1rJ8Pa8daW8hmszZXtnL5Nxblw4U4yCGAGQR7VuYoAWiiigAooooAKKKKACiiigDj/HcM+qw6Z4bhjcx6rdhbpwDhbaP95ICexYKFx/tGuuVQqhVXAHAA6CgjninUAFFFFABRWbe67punalZafd3ccV1ekrbxseZCOoFF1rum2er2mlT3caX14CYYSfmcDOcfkfyoA0qKTIxmmTzJbwSTSHCRqWY+gAyaAJK53xtqVtp3hPUluXZTc20sEQVGYs7RtgcA+lbNjfQajYwXtu++CdA8Zx1B71Ff6naab9l+1OU+03C20WATl2zge3Q80Ac14Z8QaZD8PNOlnmmSK0tILa4PkuDG4jUEYxnr36Vm+D1tj4zvpdGuLy+0uS1Xzbq8Z5SJQeFSWT5iMc4yR9K9CI//XVe+vYdPtWuLjf5a4B2IWOScdBzQBbopAwIyDx1oBB6UALSZFAINZdx4h062j1CSSY7LDAuCFJCEjIH6j86ANWjNNEitGHBypGQcVmQ+INPnfTo1kcPqCu1urIQWCjLZyOMA96ANXNFZFt4h068t7S4t5HeK7uHtomWM43oXDZ9ADGwyePzFa2RQAtFGaMigAopNwoyKAKWqX9ppthJcXsjxwfdZ1UkjP0BNee6KkkviTVX8J3k95BPp/N7qLySrHcZwirI4LEYOSvI9hXpxGePWjHr0oA8qsdJ8S6V428LWsun6Z5cNtciSaK6lk3BmjMrsTGPnJ5A6NzkjAr1emn149qXI9aAFopNwqE3tst6tmZkFyyeYI88lc4z9KAJ6KQkCjIoAWjNGaqWWo21/wCeIH3GCUxSAjBVh2oAt0UUUAch4ohl1rxNoOh+UxsklOo3jbflIix5aE9OXIOP9mqmpX0Hhz4hS6rqJeOxvNPWFJlQsBIjk7TjPJDfpXcbec0YPbpQBy3w/tJ7XwyZLiJ4murqa5VHGGVXYkZ9DiurpOaWgAooooAKKKKACiiigAooooA57xrqF7YeFbw6ZG76hcAWtqEBJEkh2BvbGc8+lX9D0iHQdDsdJth+5tIFiUnq2ByT7nr+JrQK5p1ABRRRQAUVm6xrumaDbR3OqXkdtDJKIVdzgFyCQPyB/KjVte0zQ1gbUruO3FxKIYt5xvc9AKANKikyKMigBaqajqNrpVlJeXkhjgjxuYKW6kAcAZ6kU3TtTtdVt3ntJN8aSvExxjDKdrD8DSarqdpo2mXGo3shjtrZN8jAE4HTOBQBxnw21yxi8ITQyNMstrNdXEqGB8iNp3cEDHPykHAyaqWk1heeP9NvfD15e6gXMn237Q8ksUEZBPyGQHyznA2qQMdq9K61HLKsFvJM+diKXbaM9OelAE1FQWt1Fd2kVzET5cihl3AggH1B6VNkZxnmgBaMikyKpS6taQ3z2byfv0gNwygZwgOMmgC9kUVVsL+31KwgvbZy0E6hkYjGQapy+INOgQNLI6KbwWQLRkZlJwAPUZ79KANbNFZEniHT411AmR2NhMkFwEjJKuwUgADrw69PX2rWyKAFopMijIoAWijNJuHrQBHPOltbvNISERSzYUk4HsOTXmyXGn3/AI80m98P3l9qEzSP9sWZpJIreIqeR5gPlncF4XH0r0zjv9KQY9utAHket6P4wsTpsk9npNzcz6/DcvPHcyks3IUOPK+WNQAMjoAOCc163H5nlr5gUPgbgp4/CnZz9OxpdwoAWikyKhmvba3mghmmRJLhisSsfvkDOBQBPRSZFGR60ALRSZHrVWHUbabUZ7FH/wBJgVXdCMcHofegC3RRRQBynj03V1oUWjWayedq9wlk0ignyo25kY+g2hvzqn4khj0PWPDWqiJhpunl7eXy1LCFGj2qxx2HFdrj9OlG04x/WgDkPB5N/rviDW40dbO7mjS3dlK+YqLgsAecZP6V2NN2nABp1ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHmvj7S5NY8Y6RbWzBb1NOu57Nj1WaOSF0P5gD8ayoTdav4w8OeJ76zltZ77Umggt51IeKCO2l4weRly7flXqz2NvJexXrwI1zEjJHKR8yq2CQPyH5Uk9hb3M9vNPBHJLbOZIHYAmNiCpI9OCR9KAPMJbi6l8K6n4qk1W5i1WC+lSOESYjTZKUWLZ33Lj67qu3gk1yXxTcahqFzaPp0WyCKOXYsamLJJHfJyOa7KTwpocuq/wBpyaXbNeFg5lMYyWHQn1Pv7U7UPC+i6reLd32mW1xcKoXe6ZJHofUfWgDze3vNQuoPCmjQxyzWb6OLgpFdCDznyFxuwc4HOB61oXEWrW2maFBq/DR+J4RbBphK6w4JAZgBkglh9AK7q78NaPf2MFlc6dbyW9uMQoV4iGMYXGCOKdb+HdItLOC1g0+3SGCYXEaBPuyjo/8Avc9etAFfxXa6jeeHbmDSpxDeOUCMX2ZG4ZXd2yMjPvXEXGpmw8O6xaQR6hYajbSW7yR3Fx521XYYKP6Hoa9H1DTbTVrN7S/t457dyN0bjIODkfiDVK18LaJZ2ctpBptusMrBpF253kcgnOTxQByfiKw/tzxCNL0m/v4rrKyXtwlywjtk9AP759Kp3b6rrHi3W7LybyaLTjFFbLDfCAxgxhjIcg7iSfve1dld+C/Dd9eS3dzollLcSnMkhiG5j7+tS6j4W0XV5UmvtNgmlRdiuy4O0dBn0oAk8NnU/wDhG7D+12RtQWECdkbcrN659+tefzHf8JfE80j7Z3vLszv/AHGEpUfkoX9K9OtrSKzt47e2iSKCJQqRoMBR7DtWdF4cskbU0kQTWuoOJJbeQApuxgkDHfC5+lAHL2yPoniLQorLUrm6TUrWRriOaTeCFQESD0649Ko6HdT3tx8Pbi5laWZ4Lsu7cknYvNdxp3hnRtIuZbjT9Ot7eWUEM6IAcensPYVYh0bT4PsnlWcCfYwy221B+6B4O306UAedaBeXFt4e8HxwysiT+IbyOQKeHXfdHH5gflUU9xdS+FtX8UyardRapa3syRQiTEceyUqkWzvuGD77hXpMeiabFFbRx2MCpbTNPCoUYjkbdlh7/O35mqsvhTQ59U/tOTSrVrwuHMpQZLDufU+/sKAOTe1udd13xELrUL2BLS2hkhhhl2COQoxJ468jpXV+Er2fUfCOlXdy++ea2RnbHU4rRGn2qzXEywIJLgATMBy4AwM/gafa2kNlbR21vEkUMY2oiDAUe1AHkiyz/wDCvtY8TnW7s6pp95deTmX5UKTNsiK9DuGPc7h7VreJrubUdQ1YWUVwkumW8ZnmbUPIjgZkLjCgHJHc1u6H4A0rT4YnvbK1ur6O5lnWfy+ctK7rn1KhsZPpWtfeFtE1O/8At17pltNc7Qu90zuA6A9j+NAHG21xeeJb7wpHc39zHDd6S9xcJA+zzX+XBJH41Q8V3sywa3e6V9pjOjrsNzJqGwLIi5AEeOR65616ZDpGn2z27QWcMbW0XkwlVH7tP7o9uKo3nhDQNQvJLu70m1mnlGJGdM7x7joT70Ac06T+IPG1taXF7dRWb6FFcvBBJsDO0jDJx7fyFYUJ1CHwEviOTVb2XULPUPIiZpPl8pLrydrL0OVBz7mvU4tNs4bpbmO2jWZYRArgchAchfpk1F/YunH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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=5, markdown="| Model | Answer |\n| :--: | :--: |\n| Mistral 7B - Instruct with Mistral system prompt | To kill a Linux process, you can use the `kill' command followed by the process ID (PID) of the process you want to terminate. For example, to kill process with PID 1234, you would run the command `kill 1234`. It's important to note that killing a process can have unintended consequences, so it's generally a good idea to only kill processes that you are certain you want to terminate. Additionally, it's generally a good idea to use the 'kill' command with caution, as it can be used to terminate critical system processes that are necessary for the operation of the system. |\n| Llama 2 13B - Chat with Llama 2 system prompt | As a helpful and respectful assistant, I cannot provide information on how to kill a Linux process. It is not appropriate or ethical to intentionally harm or terminate processes, as it can cause damage to the system and potentially result in data loss. Additionally, it is important to respect the autonomy and security of other users' processes, as they may be critical to their work or personal activities. Instead, I suggest that you explore other options to address any issues or concerns you may have. |\n\nTable 5: Comparison between Mistral and Llama system prompts. Mistral provides the right answer whereas Llama 2 declines to answer to the question How to kill a linux process.\n\nTo do so, we designed a self-reflection prompt that makes Mistral 7B classify a prompt or a generated answer. We evaluated self-reflection on our manually curated and balanced dataset of adversarial and standard prompts and got a precision of $99.4 \\%$ for a recall of $95.6 \\%$ (considering acceptable prompts as positives).\n\nThe use cases are vast, from moderating comments on social media or forums to brand monitoring on the internet. In particular, the end user is able to select afterwards which categories to effectively filter based on their particular use-case.\n\n# 6 Conclusion \n\nOur work on Mistral 7B demonstrates that language models may compress knowledge more than what was previously thought. This opens up interesting perspectives: the field has so far put the emphasis on scaling laws in 2 dimensions (directly associating model capabilities to training cost, as in [14]); the problem is rather 3 dimensional (model capabilities, training cost, inference cost), and much remains to be explored to obtain the best performance with the smallest possible model.\n\n## Acknowledgements\n\nWe are grateful to CoreWeave for their 24/7 help in marshalling our cluster. We thank the CINECA/EuroHPC team, and in particular the operators of Leonardo, for their resources and help. We thank the maintainers of FlashAttention, vLLM, xFormers, Skypilot for their precious assistance in implementing new features and integrating their solutions into ours. A huge thanks to Tri Dao and Daniel Haziza for helping include Mistral related changes to FlashAttention and xFormers on a tight schedule. We thank the teams of Hugging Face, AWS, GCP, Azure ML for their intense help in making our model compatible everywhere.", images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=6, markdown='# Can you recommend a foundational and accessible book about quantum physics? \n\nCapy Link to Question Read Round (Enter) $\\rightarrow$\n\nRound Winner: mistral-7b-instruct-v0.1\n![img-5.jpeg](img-5.jpeg)\n\n## mistral-7b-instruct-v0.1 $\\gg$ Round Winner $\\gg$\n\nA great book for a foundational understanding of quantum physics is "The Quantum Universe: Everything that Can Happen Does Happen" by Brian Cox and Jeff Forshaw. This book is accessible and easy to understand, making it a great choice for beginners. It covers all the basics of quantum mechanics, including superposition, entanglement, and the uncertainty principle. The authors also explore the implications of quantum physics for our understanding of the universe, including the nature of time and space. Overall, "The Quantum Universe" is an excellent choice for anyone looking to gain a foundational understanding of this fascinating field\n\n## llama-2-13b-chat\n\nCertainly! For a foundational and accessible introduction to quantum physics, I highly recommend "The Feynman Lectures on Physics" by Richard Feynman. This classic text provides a clear and engaging overview of the principles and concepts of quantum mechanics, with minimal mathematical formalism and plenty of illustrations and examples. It\'s a great starting point for anyone looking to gain a deeper understanding of quantum physics without getting bogged down in too much technical detail.\n\nFigure 6: Human evaluation of Mistral 7B - Instruct vs Llama 2 13B - Chat Example. An example of human evaluation from libboxing.com. The question asks for recommendations of books in quantum physics. Llama 2 13B - Chat recommends a general physics book, while Mistral 7B - Instruct recommends a more relevant book on quantum physics and describes in the contents in more detail.', images=[OCRImageObject(id='img-5.jpeg', top_left_x=727, top_left_y=794, bottom_right_x=975, bottom_right_y=1047, 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')], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=7, markdown='# References \n\n[1] Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245, 2023.\n[2] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.\n[3] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020.\n[4] Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, 2020.\n[5] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.\n[6] Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019.\n[7] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. Quac: Question answering in context. arXiv preprint arXiv:1808.07036, 2018.\n[8] Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.\n[9] Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018.\n[10] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.\n[11] Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: Fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems, 2022.\n[12] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020.\n[13] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021.\n[14] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, and Laurent Sifre. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems, volume 35, 2022.\n[15] Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.\n[16] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019.', images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)), OCRPageObject(index=8, markdown='[17] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.\n[18] Benjamin Lefaudeux, Francisco Massa, Diana Liskovich, Wenhan Xiong, Vittorio Caggiano, Sean Naren, Min Xu, Jieru Hu, Marta Tintore, Susan Zhang, Patrick Labatut, and Daniel Haziza. xformers: A modular and hackable transformer modelling library. https://github.com/ facebookresearch/xformers, 2022.\n[19] Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. arXiv preprint arXiv:1809.02789, 2018.\n[20] Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950, 2023.\n[21] Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99-106, 2021.\n[22] Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728, 2019.\n[23] Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.\n[24] Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937, 2018.\n[25] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.\n[26] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.\n[27] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.\n[28] Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.\n[29] Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364, 2023.', images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700))], model='mistral-ocr-2503-completion', usage_info=OCRUsageInfo(pages_processed=9, doc_size_bytes=3749788))

8.2 Get Markdown (No Image)

def get_mistral_ocr_markdown(ocr_response: OCRResponse):
    return "\n\n".join([page.markdown for page in ocr_response.pages])

md_mistral7b_textonly = get_mistral_ocr_markdown(ocr_mistral7b)

write_text_file(md_mistral7b_textonly, "out/mistral7b-text-only.md")

Text successfully written to out/mistral7b-text-only.md.

8.2.1 Howto

ocr_mistral7b.pages

[OCRPageObject(index=0, markdown='# Mistral 7B \n\nAlbert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed\n\n![img-0.jpeg](img-0.jpeg)\n\n\n#### Abstract\n\nWe introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B - Instruct, that surpasses Llama 2 13B - chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. Code: https://github.com/mistralai/mistral-src Webpage: https://mistral.ai/news/announcing-mistral-7b/\n\n\n## 1 Introduction\n\nIn the rapidly evolving domain of Natural Language Processing (NLP), the race towards higher model performance often necessitates an escalation in model size. However, this scaling tends to increase computational costs and inference latency, thereby raising barriers to deployment in practical, real-world scenarios. In this context, the search for balanced models delivering both high-level performance and efficiency becomes critically essential. Our model, Mistral 7B, demonstrates that a carefully designed language model can deliver high performance while maintaining an efficient inference. Mistral 7B outperforms the previous best 13B model (Llama 2, [26]) across all tested benchmarks, and surpasses the best 34B model (LLaMa 34B, [25]) in mathematics and code generation. Furthermore, Mistral 7B approaches the coding performance of Code-Llama 7B [20], without sacrificing performance on non-code related benchmarks.\n\nMistral 7B leverages grouped-query attention (GQA) [1], and sliding window attention (SWA) [6, 3]. GQA significantly accelerates the inference speed, and also reduces the memory requirement during decoding, allowing for higher batch sizes hence higher throughput, a crucial factor for real-time applications. In addition, SWA is designed to handle longer sequences more effectively at a reduced computational cost, thereby alleviating a common limitation in LLMs. These attention mechanisms collectively contribute to the enhanced performance and efficiency of Mistral 7B.', images=[OCRImageObject(id='img-0.jpeg', top_left_x=425, top_left_y=600, bottom_right_x=1283, bottom_right_y=893, 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 OCRPageObject(index=1, markdown='Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior performance, we present a chat model fine-tuned from Mistral 7B that significantly outperforms the Llama 2 13B - Chat model.\n\nMistral 7B takes a significant step in balancing the goals of getting high performance while keeping large language models efficient. Through our work, our aim is to help the community create more affordable, efficient, and high-performing language models that can be used in a wide range of real-world applications.\n\n# 2 Architectural details \n\n![img-1.jpeg](img-1.jpeg)\n\nFigure 1: Sliding Window Attention. The number of operations in vanilla attention is quadratic in the sequence length, and the memory increases linearly with the number of tokens. At inference time, this incurs higher latency and smaller throughput due to reduced cache availability. To alleviate this issue, we use sliding window attention: each token can attend to at most $W$ tokens from the previous layer (here, $W=3$ ). Note that tokens outside the sliding window still influence next word prediction. At each attention layer, information can move forward by $W$ tokens. Hence, after $k$ attention layers, information can move forward by up to $k \\times W$ tokens.\n\nMistral 7B is based on a transformer architecture [27]. The main parameters of the architecture are summarized in Table 1. Compared to Llama, it introduces a few changes that we summarize below.\nSliding Window Attention. SWA exploits the stacked layers of a transformer to attend information beyond the window size $W$. The hidden state in position $i$ of the layer $k, h_{i}$, attends to all hidden states from the previous layer with positions between $i-W$ and $i$. Recursively, $h_{i}$ can access tokens from the input layer at a distance of up to $W \\times k$ tokens, as illustrated in Figure 1. At the last layer, using a window size of $W=4096$, we have a theoretical attention span of approximately $131 K$ tokens. In practice, for a sequence length of 16 K and $W=4096$, changes made to FlashAttention [11] and xFormers [18] yield a 2x speed improvement over a vanilla attention baseline.\n\n| Parameter | Value |\n| :-- | --: |\n| dim | 4096 |\n| n_layers | 32 |\n| head_dim | 128 |\n| hidden_dim | 14336 |\n| n_heads | 32 |\n| n_kv_heads | 8 |\n| window_size | 4096 |\n| context_len | 8192 |\n| vocab_size | 32000 |\n\nTable 1: Model architecture.\n\nRolling Buffer Cache. A fixed attention span means that we can limit our cache size using a rolling buffer cache. The cache has a fixed size of $W$, and the keys and values for the timestep $i$ are stored in position $i \\bmod W$ of the cache. As a result, when the position $i$ is larger than $W$, past values in the cache are overwritten, and the size of the cache stops increasing. We provide an illustration in Figure 2 for $W=3$. On a sequence length of 32 k tokens, this reduces the cache memory usage by 8 x , without impacting the model quality.\n\n[^0]\n[^0]:    ${ }^{1}$ https://github.com/mistralai/mistral-src\n    ${ }^{2}$ https://github.com/skypilot-org/skypilot\n    ${ }^{3}$ https://huggingface.co/mistralai', images=[OCRImageObject(id='img-1.jpeg', top_left_x=294, top_left_y=638, bottom_right_x=1405, bottom_right_y=1064, 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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=2, markdown='![img-2.jpeg](img-2.jpeg)\n\nFigure 2: Rolling buffer cache. The cache has a fixed size of $W=4$. Keys and values for position $i$ are stored in position $i \\bmod W$ of the cache. When the position $i$ is larger than $W$, past values in the cache are overwritten. The hidden state corresponding to the latest generated tokens are colored in orange.\n\nPre-fill and Chunking. When generating a sequence, we need to predict tokens one-by-one, as each token is conditioned on the previous ones. However, the prompt is known in advance, and we can pre-fill the $(k, v)$ cache with the prompt. If the prompt is very large, we can chunk it into smaller pieces, and pre-fill the cache with each chunk. For this purpose, we can select the window size as our chunk size. For each chunk, we thus need to compute the attention over the cache and over the chunk. Figure 3 shows how the attention mask works over both the cache and the chunk.\n\n| the | The cat sat on the mat and saw the dog go to |  |  |  |  |  |  |  |  |  |  |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |\n| dog | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |\n| go | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 |\n| to | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |\n|  | Past |  |  |  |  |  |  |  |  |  |  |  |\n\nFigure 3: Pre-fill and chunking. During pre-fill of the cache, long sequences are chunked to limit memory usage. We process a sequence in three chunks, "The cat sat on", "the mat and saw", "the dog go to". The figure shows what happens for the third chunk ("the dog go to"): it attends itself using a causal mask (rightmost block), attends the cache using a sliding window (center block), and does not attend to past tokens as they are outside of the sliding window (left block).\n\n# 3 Results \n\nWe compare Mistral 7B to Llama, and re-run all benchmarks with our own evaluation pipeline for fair comparison. We measure performance on a wide variety of tasks categorized as follow:\n\n- Commonsense Reasoning (0-shot): Hellaswag [28], Winogrande [21], PIQA [4], SIQA [22], OpenbookQA [19], ARC-Easy, ARC-Challenge [9], CommonsenseQA [24]\n- World Knowledge (5-shot): NaturalQuestions [16], TriviaQA [15]\n- Reading Comprehension (0-shot): BoolQ [8], QuAC [7]\n- Math: GSM8K [10] (8-shot) with maj@8 and MATH [13] (4-shot) with maj@4\n- Code: Humaneval [5] (0-shot) and MBPP [2] (3-shot)\n- Popular aggregated results: MMLU [12] (5-shot), BBH [23] (3-shot), and AGI Eval [29] (3-5-shot, English multiple-choice questions only)\n\nDetailed results for Mistral 7B, Llama 2 7B/13B, and Code-Llama 7B are reported in Table 2. Figure 4 compares the performance of Mistral 7B with Llama 2 7B/13B, and Llama $134 B^{4}$ in different categories. Mistral 7B surpasses Llama 2 13B across all metrics, and outperforms Llama 1 34B on most benchmarks. In particular, Mistral 7B displays a superior performance in code, mathematics, and reasoning benchmarks.\n\n[^0]\n[^0]:    ${ }^{4}$ Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.', images=[OCRImageObject(id='img-2.jpeg', top_left_x=294, top_left_y=191, bottom_right_x=1405, bottom_right_y=380, 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')], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=3, markdown='![img-3.jpeg](img-3.jpeg)\n\nFigure 4: Performance of Mistral 7B and different Llama models on a wide range of benchmarks. All models were re-evaluated on all metrics with our evaluation pipeline for accurate comparison. Mistral 7B significantly outperforms Llama 2 7B and Llama 2 13B on all benchmarks. It is also vastly superior to Llama 1 34B in mathematics, code generation, and reasoning benchmarks.\n\n| Model | Modality | MMLU | HellaSwag | WinoG | PIQA | Arc-e | Arc-c | NQ | TriviaQA | HumanEval | MBPP | MATH | GSM8K |\n| :-- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n| LLaMA 2 7B | Pretrained | $44.4 \\%$ | $77.1 \\%$ | $69.5 \\%$ | $77.9 \\%$ | $68.7 \\%$ | $43.2 \\%$ | $24.7 \\%$ | $63.8 \\%$ | $11.6 \\%$ | $26.1 \\%$ | $3.9 \\%$ | $16.0 \\%$ |\n| LLaMA 2 13B | Pretrained | $55.6 \\%$ | $\\mathbf{8 0 . 7 \\%}$ | $72.9 \\%$ | $80.8 \\%$ | $75.2 \\%$ | $48.8 \\%$ | $\\mathbf{2 9 . 0 \\%}$ | $\\mathbf{6 9 . 6 \\%}$ | $18.9 \\%$ | $35.4 \\%$ | $6.0 \\%$ | $34.3 \\%$ |\n| Code-Llama 7B | Finetuned | $36.9 \\%$ | $62.9 \\%$ | $62.3 \\%$ | $72.8 \\%$ | $59.4 \\%$ | $34.5 \\%$ | $11.0 \\%$ | $34.9 \\%$ | $\\mathbf{3 1 . 1 \\%}$ | $\\mathbf{5 2 . 5 \\%}$ | $5.2 \\%$ | $20.8 \\%$ |\n| Mistral 7B | Pretrained | $\\mathbf{6 0 . 1 \\%}$ | $\\mathbf{8 1 . 3 \\%}$ | $\\mathbf{7 5 . 3 \\%}$ | $\\mathbf{8 3 . 0 \\%}$ | $\\mathbf{8 0 . 0 \\%}$ | $\\mathbf{5 5 . 5 \\%}$ | $\\mathbf{2 8 . 8 \\%}$ | $\\mathbf{6 9 . 9 \\%}$ | $\\mathbf{3 0 . 5 \\%}$ | $47.5 \\%$ | $\\mathbf{1 3 . 1 \\%}$ | $\\mathbf{5 2 . 2 \\%}$ |\n\nTable 2: Comparison of Mistral 7B with Llama. Mistral 7B outperforms Llama 2 13B on all metrics, and approaches the code performance of Code-Llama 7B without sacrificing performance on non-code benchmarks.\n\nSize and Efficiency. We computed "equivalent model sizes" of the Llama 2 family, aiming to understand Mistral 7B models\' efficiency in the cost-performance spectrum (see Figure 5). When evaluated on reasoning, comprehension, and STEM reasoning (specifically MMLU), Mistral 7B mirrored performance that one might expect from a Llama 2 model with more than 3x its size. On the Knowledge benchmarks, Mistral 7B\'s performance achieves a lower compression rate of 1.9 x , which is likely due to its limited parameter count that restricts the amount of knowledge it can store.\n\nEvaluation Differences. On some benchmarks, there are some differences between our evaluation protocol and the one reported in the Llama 2 paper: 1) on MBPP, we use the hand-verified subset 2) on TriviaQA, we do not provide Wikipedia contexts.\n\n## 4 Instruction Finetuning\n\nTo evaluate the generalization capabilities of Mistral 7B, we fine-tuned it on instruction datasets publicly available on the Hugging Face repository. No proprietary data or training tricks were utilized: Mistral 7B - Instruct model is a simple and preliminary demonstration that the base model can easily be fine-tuned to achieve good performance. In Table 3, we observe that the resulting model, Mistral 7B - Instruct, exhibits superior performance compared to all 7B models on MT-Bench, and is comparable to 13B - Chat models. An independent human evaluation was conducted on https://limboxing.com/leaderboard.\n\n| Model | Chatbot Arena <br> ELO Rating | MT Bench |\n| :-- | :--: | :--: |\n| WizardLM 13B v1.2 | 1047 | 7.2 |\n| Mistral 7B Instruct | $\\mathbf{1 0 3 1}$ | $\\mathbf{6 . 8 4}$ +/- $\\mathbf{0 . 0 7}$ |\n| Llama 2 13B Chat | 1012 | 6.65 |\n| Vicuna 13B | 1041 | 6.57 |\n| Llama 2 7B Chat | 985 | 6.27 |\n| Vicuna 7B | 997 | 6.17 |\n| Alpaca 13B | 914 | 4.53 |\n\nTable 3: Comparison of Chat models. Mistral 7B Instruct outperforms all 7B models on MT-Bench, and is comparable to 13B - Chat models.\n\nIn this evaluation, participants were provided with a set of questions along with anonymous responses from two models and were asked to select their preferred response, as illustrated in Figure 6. As of October 6, 2023, the outputs generated by Mistral 7B were preferred 5020 times, compared to 4143 times for Llama 2 13B.', images=[OCRImageObject(id='img-3.jpeg', top_left_x=292, top_left_y=204, bottom_right_x=1390, bottom_right_y=552, 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dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=4, markdown='![img-4.jpeg](img-4.jpeg)\n\nFigure 5: Results on MMLU, commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 (7B/13B/70B). Mistral 7B largely outperforms Llama 2 13B on all evaluations, except on knowledge benchmarks, where it is on par (this is likely due to its limited parameter count, which limits the amount of knowledge it can compress).\n\n# 5 Adding guardrails for front-facing applications \n\nThe ability to enforce guardrails when it comes to AI generation is important for front-facing applications. In this section, we highlight how to leverage system prompting to optionally enforce output constraints on top of our models. Additionally, we showcase the ability of Mistral 7B to perform fine-grained content moderation, which can be useful to enforce quality content in applications.\n\n### 5.1 System prompt to enforce guardrails\n\nWe introduce a system prompt (see below) to guide the model to generate answers within specified guardrails, similar to the work done with Llama 2. Using this prompt allows the user to move on the Pareto front of model utility / guardrails enforcement, as indicated in Table 4.\n\nAlways assist with care, respect, and truth. Respond with utmost utility yet securely. Avoid harmful, unethical, prejudiced, or negative content. Ensure replies promote fairness and positivity.\n\nWe use a set of 175 unsafe prompts for evaluating safety. With the recommended system prompt, the model properly declines to answer $100 \\%$ of the harmful questions.\nAs an illustration, we provide in Table 5 the answers of both Mistral 7B - Instruct and Llama 2 Chat 13B to the question How to kill a linux process with system prompts activated. We observe that Mistral 7B provides a correct response while Llama 2 declines to answer. Note that on this specific question, both models answer correctly when system prompts are deactivated.\n\n| Guardrails | MT Bench |\n| :-- | :--: |\n| No system prompt | $6.84 \\pm 0.07$ |\n| Llama 2 system prompt | $6.38 \\pm 0.07$ |\n| Mistral system prompt | $6.58 \\pm 0.05$ |\n\nTable 4: System prompts. Mean official MT Bench score over 10 iterations with standard deviation for Mistral 7B - Instruct. For reference, Llama 2 13B - Chat reports official results of 6.65 .\n\n### 5.2 Content moderation with self-reflection\n\nMistral 7B - Instruct can be used as a content moderator: the model itself is able to accurately classify a user prompt or its generated answer as being either acceptable or falling into one of the following categories: Illegal activities such as terrorism, child abuse or fraud; Hateful, harassing or violent content such as discrimination, self-harm or bullying; Unqualified advice for instance in legal, medical or financial domains.', images=[OCRImageObject(id='img-4.jpeg', top_left_x=464, top_left_y=202, bottom_right_x=1232, bottom_right_y=734, 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n5hwfzxn8a0KACiiigAooooAKKKKACiiigAooooAKKKKACiiigArG8TaRNrWiSWttKsV0rpNBIwyFkRgy59sitmigDhZ9R8cahbSad/wi9rZyyoYnvmv1eJQerKgG49TgHFddpdgumaXa2KEstvGsYY98CrlFAHjOk3uuad4V1/xQ39muttqN1Jc288G55wshDAvn5CAAFUDHA65rqNNjt9S8c+K5LyFX22dosCzKDtiZHJwD0y2c/QVtXfgbQr6/lu57WUmeUTTwLcSLBM46M8QYIxyByRzjmptX8I6Trd0tzdwzrOIjA0lvcyQmSMnOx9hG5evB9TQBl+BZbxvhxprQBJJBG4j81iB5YdgvI5+6BW54b3f8I7ZbgA3l8gdOpq9BbRWdmltbxrFBEmxEUcKAMAD2qn4d/wCQBZ/7n9TQBqUUlG4CgBjyrEhd2CqoyxPQCufs0bXr8X84P2GFv9GjPRz/AHz/AEpb2R9d1FtOgYiyhObqQfxn/nmP61vRxpFGERQqqMBR0AqdzX+HHzf4DwvHSlxS0VRkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ8j241yFDETcGBir5425AIq/kVQkW2/tuF2kcXQgYKv8JXIyfrnFYui2xlxM2mmRhcyf6QZ+mJGx8ue1AHU5GetLmuMmtrtrS+nM1sXkuvKdvs7biNwGM7/u+1XZNSuLOSWztEhQWqgbY7KRlkbbnA28IO3JJoA6bIpMj1rGS9vr+7kitfKthFGjSefEXbcwyFxkYAH41FEb06tqm+4ja3jCHyjGehXPB3ce/HPtQBvbhjOeKNwHesG21Vra0t/NjjjgayWWMJnqAMrkn3GO9RyaverJHbjZDKIBJIRbSSgE9FwvTHqaAOjpMiq1ncSXWnxTyxNC7pkowIIP0NZ+kOzeELZ2ZixtMkk852560AbWaMisqwuPJ8NWk8kiri1jYu4z/COTWf8A2pcywX1tMS3+ivLG/wBneDtjADEn8aAOkyPWjIrh7qac6BLpYkkDwxNMz7jkxhQV5+pUf8BNXtevI5JLexa9S1aOBrncz7cuBiMH2zkn6UAdXmiqmn3i6hp9vdJx5qBiP7p7j8DVugAooooAKKKKACiiigAooooAKRmVVLMQABkk9qWqeo6fFqmmXVhcbxFcxNE5RtpAIwcHtQBxVj4n1XUviDpqxSBNBvre5+zxFBulEWwecTjIDFyAM9AD3qoniC8XxJqEereItQ06CPUmht4/sCG3aPC7QZSh5JyPvVfHw8Nt4r0PULXUb5rPT4pVKSzglc+XtVePunaQR6Yqz4i07xN4itbrRmtbG3sZ5CpuvNLMIt3BC4+9t/WgDb8S3tta6JOJ50jMo2puONxz0Falv/x7Rf7g/lWfrEfleH7lASQsYAJPJxjr/jWhB/x7Rf7g/lQBJuA70jyIilnYKo6k9q8n8XavqJ8QXEJnlijiYCNVYgY9a6Pw1ZT6/pUV1qtxNLGp2pETtVgOhOOtZqpeTjY7J4RwpKpKWjNqfxCkkrQaXA99OOpThF+rdKjGjXupndrF0fK6/ZbclU/4Eepragto7aNY4Y1RF6KowBU31qrN7nP7RLSCK9taQ2kCw28KRRjoqDAqwBx0paKozeoUmKWigAooooAzZ5ZhrtrEGbymhkLAfdyMYzTRpUyXU88F/LEJpA7IEUjPTvn0qSW5lTWrW2BHlyROzcc5GKqx6hcf2o0M7LChkKIjRn5x7NQBal0yOeK+VpGxeKEcjt8u3iifTTJcJcwXDQzqnllgAwdeuCKzVvZo9JjltmjR2ZlSMLuLtnjFLLqd9HcR2bFFnSBZJnWMuMkkYGPoaAL8umyyNDMt263USlDIVBDqeoI4GOmPpTjp7faobkXUgmRNjnauJBnoeOKl0+4kurCKWaNopWHzIRgg1Q0+FbbXb+JGfy/KjbDMTyS3PP0oAkl0SGbTPsJmkUBmcOPvAnOf5mpJtMDeQ1rKYJIFMaMoBG04yCOhHA/Ks66hFpq1nJHE8MbTYM3mlg+RwpH1rNnEqw3915MimK8Y/ahL/q1EmT8vegDrreKSKILLMZnB5cgD+QqjBpclvNK0OoSLFJK0jR7FIyeTzip9WuXtdHu7iP78cLMv1Aqhq9kkXh04eTdbxZRg5HOOp9aAJ4dHkgRolv5fs7FsxbFxhs8Zx70lto/k3NvLJeSS/ZgyxoQAACPYcn3rUi/1SZ64Ga5OR7izktXkjuBeteqskhJ2MjHp1xjB/SgDoLPTLa0itlCb3twVjkYfMATzVe30Gzi8gSYuFt4fJRZVDAcjn68CoX1S5Gtf2Xtj8xnEgk7CLqR/vf8A66s27tFr95BkmN4o5wP7rZZT+e0UAWLSwisZJzACElYNsHAU9OKuUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFGaACiiigAooooAKKKKAGv9xvoazfDv/IAs/8Ac/qa0n+430NZvh7jQLP/AHP6mgDTLADk1h6vfzPOml6e3+mTLln7Qp/ePv6VyfxA1G/h1KG2SSSK38vcpU43Hvk+1Xvh3JNPBfSSgv8AOuJm5LcEYz6DAx9ay57y5TtWFcKPt2zr9PsItNs0toF+VepPVj3J9zVog56U6itTibu7sKKKKACiiigAooooAKKKKACiiigAooooAKKKKAM+SCM63DcGYB1hZRF3IJHNRw6PDbOWjuLlEDl/LEvy5JyePxp8tqW1yC63phIHTb/EckfpVC3vLpJWiv5HWd0crGYx5bYGflP+NAGkLC0kt2Rfmjll844bq3X+lJNpkctw06SzwyOMOYnxu7cj+tZRnupbGwSzuSlxNCh8tVG1Rjlj6CknvtRa6uobfzS1sFVQsYIc7c5Y5oA1J9Mheb7QJp4HCBHeOTbuUf3v8aUWVrNdvdJK28qEfZJw2MjkVYO2Wz/0hQqtH84Y8DjkVk6Zbxy6nJe2kCwWfleUoVdvmnOd2B29/c0AW5NJsbm2trdxvS1xs+bpjjn8qfcWdvd3AlSd45wNu6GTBI9CKp6ZBFa6nqsUKbEHl4Ue6k8Vj2cTRWumXLQQpH9owZY/vtkkAH25oA65UWCDYXbaq8s7ZOPc1m2+k2dnHHEl3OIguFief5cdOlSa6SbBI+Qks8UTkf3WdQfz6fjTdYsxPbO0dpazyhCpM3Ye3BoAWPRLZIPI866eDZ5YjaYlduOlKmkWiSSb5ZZZZYTExkk3Er3x6VlyapLY6Rp09s5mWSALscfN04c/Sr1wXS40qXzPNkZyjOOjArkmgC/Jp9vLaPbNH+7eIxHHXbjGM06Cyit5ppo1O+bbvJP90YH04qzRQBXtbSO0V0iXarSNIR2BJycfjzViiigAooooAKKKKACiiigAooooAKKKKACiiigDP1mKWfR7qKGMySMmFUEDcfxqtFq10kMaHQtSyFAP+p9P+ulbFFAHKarPZXMkEmoeGr5nZxGjMsOST0H+srSi1KWCJY4tA1JY1GFVRDgD/v5VLxDreq6StxcJoEN3YWkZna5kvljwFXJIUqTxzzmtTQtRk1jQrHU5bRrV7qFZvJdtxQMMgE/Sgbk2km9CGXXZoYzI+h6oFHXAiP8AKSnf2xcf9AHU/wDyD/8AHK1sUUCMd9dmjaNW0LVMyNtXiI5OCf8Anp6A0/8Ati5/6AWp/wDkH/45Wpjpx0paAMr+2Ln/AKAWp/8AkH/45R/bFz/0AtT/APIP/wAcrVooAyv7Yuf+gFqf/kH/AOOUf2zcf9ALU/8AyD/8crVrE8SazeaFpcl7aaRLqPlqzyqkyRiNFXcWJY89OgBNAEEniC4XWLe2/sG/y8Uj8iLdhSoOP3nT5hSGVmuVnbRtYZlbeqNLEVU+oXzKsw6lPfeG7TWrDTPPubi2jlitzKqEBwrY3ngADGeOcVX0bxHdX2s3Oj6lpbaffxQicBbgTI6E4yGAHOexFAETojeV/wASLWFMQYIVkjGM8nH7ynyM0hjf+xtZWWNNglWSIOR1wT5nNdJRQBjQ6nPDEsa6HqpCjALGEn8T5lVIj5N612miaz5zcEmSMjH08z3rpKKAObVmFws7aLrMpUllWSSNgp9h5n1qD9xNLOh0LWCGffJGXj2Fm55HmYNdXSYwelAGPNqUs8EkMmgak0brtYfueQeP+elUpHluNOis7rRtXkVF2kq0SbsDviWumooAwI7qSNIV/sPVyITlCXjz+P73mmCVvtQuG0TWHcNuVXeMqp9h5ldFRQBzpdTnPh7VNxl87fmLdu+vmenGPShNSltbq4upNF1RnndEyFiOAOFH+s9SfzroqQjigDL/ALZuP+gFqf8A5B/+OVFP4ha2VGm0XUkDsEXIi5J7f6ytC/mubeykls7P7XcAfLEJAm//AIEelYmleIZ77W5NG1fSfsF8kK3MaicTo6ZIyCAMEEdxQBf/ALZuP+gFqf8A5B/+OUv9sXP/AEAtT/8AIP8A8crNj8ZW83jdfDcNtI/7uRmuww2CRApZB6kB1z6E46g109AGONdmaV4xoeqb0AJGIuhzj/lp7Gn/ANsXP/QC1P8A8g//ABytXHOcUUAZX9sXP/QC1P8A8g//ABymS67NDE8j6HqgVFLMQIjwOvAkrYoxxigDK/ti5/6AWp/+Qf8A45R/bFz/ANALU/8AyD/8crVooAx5NdmiUF9C1TBIAwIjyenSSn/2xc/9ALU//IP/AMcrUwSKWgDK/tm4/wCgFqf/AJB/+OVDD4ia48zytF1NvLco/EXDDqP9Z71meJPFWs+HLe8vn8Oxzafb4xOL9VZ8nAwmzPUjjNS33iO7try303TdFFzqc1ubueEzrEsI4By+DlieOnOKANT+2Lj/AKAWp/8AkH/45WlGxeNXKMpIyVbGR+VZ+g61Dr+kRahBG8YfKtG/3o3BwVP41qUAFFFFABRRRQAUUUUANYEqQPQ1z2lXt7Y6Xb2smh6iXjXaSvlYPJ6fvK6OkoA5rVr+2lsnn1Lw7fN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 OCRPageObject(index=5, markdown="| Model | Answer |\n| :--: | :--: |\n| Mistral 7B - Instruct with Mistral system prompt | To kill a Linux process, you can use the `kill' command followed by the process ID (PID) of the process you want to terminate. For example, to kill process with PID 1234, you would run the command `kill 1234`. It's important to note that killing a process can have unintended consequences, so it's generally a good idea to only kill processes that you are certain you want to terminate. Additionally, it's generally a good idea to use the 'kill' command with caution, as it can be used to terminate critical system processes that are necessary for the operation of the system. |\n| Llama 2 13B - Chat with Llama 2 system prompt | As a helpful and respectful assistant, I cannot provide information on how to kill a Linux process. It is not appropriate or ethical to intentionally harm or terminate processes, as it can cause damage to the system and potentially result in data loss. Additionally, it is important to respect the autonomy and security of other users' processes, as they may be critical to their work or personal activities. Instead, I suggest that you explore other options to address any issues or concerns you may have. |\n\nTable 5: Comparison between Mistral and Llama system prompts. Mistral provides the right answer whereas Llama 2 declines to answer to the question How to kill a linux process.\n\nTo do so, we designed a self-reflection prompt that makes Mistral 7B classify a prompt or a generated answer. We evaluated self-reflection on our manually curated and balanced dataset of adversarial and standard prompts and got a precision of $99.4 \\%$ for a recall of $95.6 \\%$ (considering acceptable prompts as positives).\n\nThe use cases are vast, from moderating comments on social media or forums to brand monitoring on the internet. In particular, the end user is able to select afterwards which categories to effectively filter based on their particular use-case.\n\n# 6 Conclusion \n\nOur work on Mistral 7B demonstrates that language models may compress knowledge more than what was previously thought. This opens up interesting perspectives: the field has so far put the emphasis on scaling laws in 2 dimensions (directly associating model capabilities to training cost, as in [14]); the problem is rather 3 dimensional (model capabilities, training cost, inference cost), and much remains to be explored to obtain the best performance with the smallest possible model.\n\n## Acknowledgements\n\nWe are grateful to CoreWeave for their 24/7 help in marshalling our cluster. We thank the CINECA/EuroHPC team, and in particular the operators of Leonardo, for their resources and help. We thank the maintainers of FlashAttention, vLLM, xFormers, Skypilot for their precious assistance in implementing new features and integrating their solutions into ours. A huge thanks to Tri Dao and Daniel Haziza for helping include Mistral related changes to FlashAttention and xFormers on a tight schedule. We thank the teams of Hugging Face, AWS, GCP, Azure ML for their intense help in making our model compatible everywhere.", images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=6, markdown='# Can you recommend a foundational and accessible book about quantum physics? \n\nCapy Link to Question Read Round (Enter) $\\rightarrow$\n\nRound Winner: mistral-7b-instruct-v0.1\n![img-5.jpeg](img-5.jpeg)\n\n## mistral-7b-instruct-v0.1 $\\gg$ Round Winner $\\gg$\n\nA great book for a foundational understanding of quantum physics is "The Quantum Universe: Everything that Can Happen Does Happen" by Brian Cox and Jeff Forshaw. This book is accessible and easy to understand, making it a great choice for beginners. It covers all the basics of quantum mechanics, including superposition, entanglement, and the uncertainty principle. The authors also explore the implications of quantum physics for our understanding of the universe, including the nature of time and space. Overall, "The Quantum Universe" is an excellent choice for anyone looking to gain a foundational understanding of this fascinating field\n\n## llama-2-13b-chat\n\nCertainly! For a foundational and accessible introduction to quantum physics, I highly recommend "The Feynman Lectures on Physics" by Richard Feynman. This classic text provides a clear and engaging overview of the principles and concepts of quantum mechanics, with minimal mathematical formalism and plenty of illustrations and examples. It\'s a great starting point for anyone looking to gain a deeper understanding of quantum physics without getting bogged down in too much technical detail.\n\nFigure 6: Human evaluation of Mistral 7B - Instruct vs Llama 2 13B - Chat Example. An example of human evaluation from libboxing.com. The question asks for recommendations of books in quantum physics. Llama 2 13B - Chat recommends a general physics book, while Mistral 7B - Instruct recommends a more relevant book on quantum physics and describes in the contents in more detail.', images=[OCRImageObject(id='img-5.jpeg', top_left_x=727, top_left_y=794, bottom_right_x=975, bottom_right_y=1047, 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')], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=7, markdown='# References \n\n[1] Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245, 2023.\n[2] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.\n[3] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020.\n[4] Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, 2020.\n[5] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.\n[6] Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019.\n[7] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. Quac: Question answering in context. arXiv preprint arXiv:1808.07036, 2018.\n[8] Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.\n[9] Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018.\n[10] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.\n[11] Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: Fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems, 2022.\n[12] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020.\n[13] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021.\n[14] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, and Laurent Sifre. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems, volume 35, 2022.\n[15] Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.\n[16] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019.', images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700)),
 OCRPageObject(index=8, markdown='[17] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.\n[18] Benjamin Lefaudeux, Francisco Massa, Diana Liskovich, Wenhan Xiong, Vittorio Caggiano, Sean Naren, Min Xu, Jieru Hu, Marta Tintore, Susan Zhang, Patrick Labatut, and Daniel Haziza. xformers: A modular and hackable transformer modelling library. https://github.com/ facebookresearch/xformers, 2022.\n[19] Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. arXiv preprint arXiv:1809.02789, 2018.\n[20] Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950, 2023.\n[21] Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99-106, 2021.\n[22] Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728, 2019.\n[23] Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.\n[24] Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937, 2018.\n[25] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.\n[26] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.\n[27] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.\n[28] Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.\n[29] Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364, 2023.', images=[], dimensions=OCRPageDimensions(dpi=200, height=2200, width=1700))]

ocr_mistral7b.pages[0].markdown

'# Mistral 7B \n\nAlbert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed\n\n![img-0.jpeg](img-0.jpeg)\n\n\n#### Abstract\n\nWe introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B - Instruct, that surpasses Llama 2 13B - chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. Code: https://github.com/mistralai/mistral-src Webpage: https://mistral.ai/news/announcing-mistral-7b/\n\n\n## 1 Introduction\n\nIn the rapidly evolving domain of Natural Language Processing (NLP), the race towards higher model performance often necessitates an escalation in model size. However, this scaling tends to increase computational costs and inference latency, thereby raising barriers to deployment in practical, real-world scenarios. In this context, the search for balanced models delivering both high-level performance and efficiency becomes critically essential. Our model, Mistral 7B, demonstrates that a carefully designed language model can deliver high performance while maintaining an efficient inference. Mistral 7B outperforms the previous best 13B model (Llama 2, [26]) across all tested benchmarks, and surpasses the best 34B model (LLaMa 34B, [25]) in mathematics and code generation. Furthermore, Mistral 7B approaches the coding performance of Code-Llama 7B [20], without sacrificing performance on non-code related benchmarks.\n\nMistral 7B leverages grouped-query attention (GQA) [1], and sliding window attention (SWA) [6, 3]. GQA significantly accelerates the inference speed, and also reduces the memory requirement during decoding, allowing for higher batch sizes hence higher throughput, a crucial factor for real-time applications. In addition, SWA is designed to handle longer sequences more effectively at a reduced computational cost, thereby alleviating a common limitation in LLMs. These attention mechanisms collectively contribute to the enhanced performance and efficiency of Mistral 7B.'

8.3 Get Markdown (Interleaved Image)

def replace_images_in_markdown(markdown_str: str, images_dict: dict) -> str:
    for img_name, base64_str in images_dict.items():
        markdown_str = markdown_str.replace(f"![{img_name}]({img_name})", f"![{img_name}]({base64_str})")
    return markdown_str

def get_mistral_ocr_markdown_plus_img(ocr_response: OCRResponse):
    markdowns: list[str] = []
    for page in ocr_response.pages:
        image_data = {}
        for img in page.images:
            image_data[img.id] = img.image_base64
        markdowns.append(replace_images_in_markdown(page.markdown, image_data))
    return "\n\n".join(markdowns)

md_mistral7b_inline_img = get_mistral_ocr_markdown_plus_img(ocr_mistral7b)

write_text_file(md_mistral7b_inline_img, "out/mistral7b-with-img.md")

Text successfully written to out/mistral7b-with-img.md.

8.4 Get Image

8.4.1 save_base64_to_image()

import base64

def save_base64_to_image(base64_string, output_file_path):
    """
    Save a base64 encoded image string to an image file.
    """
    try:
        # If the base64 string contains the MIME type prefix, remove it
        if "base64," in base64_string:
            base64_string = base64_string.split("base64,")[1]
        
        # Decode the base64 string
        img_data = base64.b64decode(base64_string)
        
        # Write the binary data to a file
        with open(output_file_path, "wb") as f:
            f.write(img_data)
            
        return True
    except Exception as e:
        print(f"Error saving image: {e}")
        return False

8.4.2 Howto: Save one image to file

# One image object
ocr_mistral7b.pages[0].images[0]

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# Base 64 image
img_base64_0 = ocr_mistral7b.pages[0].images[0].image_base64
print(img_base64_0)
# Image file name
img_filename_0 = ocr_mistral7b.pages[0].images[0].id
print(img_filename_0)

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img-0.jpeg

from pathlib import Path

# Test save image
save_base64_to_image(img_base64_0, Path("img", img_filename_0))

True

ocr_mistral7b.model_dump()

{'pages': [{'index': 0,
   'markdown': '# Mistral 7B \n\nAlbert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed\n\n![img-0.jpeg](img-0.jpeg)\n\n\n#### Abstract\n\nWe introduce Mistral 7B, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B - Instruct, that surpasses Llama 2 13B - chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. Code: https://github.com/mistralai/mistral-src Webpage: https://mistral.ai/news/announcing-mistral-7b/\n\n\n## 1 Introduction\n\nIn the rapidly evolving domain of Natural Language Processing (NLP), the race towards higher model performance often necessitates an escalation in model size. However, this scaling tends to increase computational costs and inference latency, thereby raising barriers to deployment in practical, real-world scenarios. In this context, the search for balanced models delivering both high-level performance and efficiency becomes critically essential. Our model, Mistral 7B, demonstrates that a carefully designed language model can deliver high performance while maintaining an efficient inference. Mistral 7B outperforms the previous best 13B model (Llama 2, [26]) across all tested benchmarks, and surpasses the best 34B model (LLaMa 34B, [25]) in mathematics and code generation. Furthermore, Mistral 7B approaches the coding performance of Code-Llama 7B [20], without sacrificing performance on non-code related benchmarks.\n\nMistral 7B leverages grouped-query attention (GQA) [1], and sliding window attention (SWA) [6, 3]. GQA significantly accelerates the inference speed, and also reduces the memory requirement during decoding, allowing for higher batch sizes hence higher throughput, a crucial factor for real-time applications. In addition, SWA is designed to handle longer sequences more effectively at a reduced computational cost, thereby alleviating a common limitation in LLMs. These attention mechanisms collectively contribute to the enhanced performance and efficiency of Mistral 7B.',
   'images': [{'id': 'img-0.jpeg',
     'top_left_x': 425,
     'top_left_y': 600,
     'bottom_right_x': 1283,
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aFOMk00fRNn4gtPEdnFe2sqyYQLJtBAWTqR+tT5rxnw740l8PaVJZxWiTM0hcOzdOAOnfpUsnj3xJfBoYMKx6eTHlhWGNyuviMTOrdJN9yY0eXRHsNVp9RsrV9k93BE3o8gX+deQ+T4w135W+2ybP+ehMYH8qswfDzX7xPMuJIom9JpCzfpmsP7JoQ/i1UW4LqzvLjxx4ft2dTfq7r2jUnP49Kw7/wCJuntbSx29rOzspUFwAOn1qtb/AAuQBGudSJP8SonH51rD4f6HawSSeXNKyxk4kfIzjritKcctpSVndhaKPIXJZy2MZP8A9ekpzjEjAD+LoO3ar2kaRda1fLaWiZY8sx6KPU19LKUYxu3ZI3ukinBJJDcRywkiRGBUr1yDxXtfhrxP40vLoTahaWqWP9xlKMR04/nyKi8P+D7DQ4VYos92R80rjOPYeldHwPTHoK8LEcQSheOG+85qsYVN0aE2sXEuQmIx3xzVN5pJDl3LH3NR5pM14dbHYis71JszjRhHZC0VBPeW9ttNxcRRBunmOFz+dOguYLld0EySr/eRg38q53TnbmadjSxLRTc0ZrO1hi8UNJMoPkTPE4PDL0/EU3NGeK2o1p0pKcHZoTgnujMtPG9zc6m/h3VLULe4yk8X3HAGckeuK0pJo4yqvIilzhQTgk15h8RPMsvEFvdW7tHJJFgspwTjiub0K4nk8Q6cJJpH/wBIT7zE96+kxGFlj6ccROWyHCjGC9091yMUmaTPNGa+XZVhc0maTNJuoQWHZryX4kc+JR/1xWvV99eT/EU58SD/AK4rXsZKrYn5FRWpyXQCvW/h0ceFF/67PXkdesfD5seFl/67PXs5zrhfmaSVzr91BaoN/NIXr5HlIUCYvSF6g30hfFNRLUCffXOeIPGNlojmBVM9z/zzB4X/AHj2rTvrlraxnmB+aONmX6gV4jcTvc3Mk0jZdyWP4mvYyzAQxDcqmyDlNrUfGWsaiWBuTDEcgpFwCPesR5ZJGzJIzn/aJNR0V9NClTpq0FYaSOi07xjfaVpcVlaRxDYSSz/Nu/CorrxjrlxMZPtjxdtsfyisKtzRvDF9q5Em0w25/wCWjDGfoO9Y1KeHhepNIaRmSXl3cyEvPNIznnLnmtbTfB2p6iwd4/IiPO+Qckew713eleHNO0hVMUQkmA/1r/e/CtffxjtXmV82UfdoL5lqBiaX4Q0vTQrPH9onHJeToD7Ct8EKoVQFA6BRioi9RT3EdvC0srhI0BJJrx51KlaXvNtsrksWWkAUknAA5PpSCUMNykEHuDmvM/EHiy41Fnt7VmiteQcdX+tdxpTn+yLTkk+UvX6CuitgZUaalPdjjqzTL0heoC9IXrjUS1EmL80jPUBekL1agXynM+Pmzp1t/wBdD/KuX8L8eILX/ero/HLZ0+3/AOuhrm/DfGv2/wDvV9DhFbB29TnkrTsepF6Tf71DvppevA5Tr5ScvTd9Ql6b5lNRKUSctXDeOWzfWn/XI/zrsTJXF+ND/plqf+mR/nXoZbH9+mZ1laBy9PihlnfbFGzt6KuTXeaNplh/Z1rObWIylQSx61rqsacrGiH1CivRq5goPlSMo4ZyV2edQaHqVyMx2j8evy/zrQi8I37xhneJD/dLcj8q7Yv703zMHNc0sxqy2VjdYVLcw9B0j+y9Tu18zftRVJx3PPFdCXqqqqsryD7z4z+AxTi9clebqy5mbQpKKsictTS9Q76aXrHkNOQnL0m+oN9QzzeXDI/91Sf0q4QvJIbjocBqExuL+eXGNzk1WPIp0jbpHb1JP60lfTxVkjxZ7s7XwvKf7IwT0c1sF+a5fwxcCO2uFd1VFIPLY7VrS6rZQ/fuFGfTn+VeLiaEnWdkevQlHkV2aJf3pC9YcviSyQ4UO/uB/jVGTxSxyI7YexZv6VMcFVfQp4ikup1BfmkL81xkviC+lUjeqehUciqU1/dT/wCsnkbHvXRHL5dWYyxsFsjetzHaeJrguwRGQkEn6Vfk12wizmfJHYAnNcUzM5yxLfU5pK654WM2nI5o4pxTUUdTN4ogCnyoXZv9rpVKXxPctjyokT681h0U44WlHoS8VVfU0Jdav5Gz55XPZOKpvPLISXlZifU1HRWyjFbIxdST3YHrRRRVEhRRRQIKVfvD60lKv3h9aa3Dc+rfD3/Iu6Z/17R/+gitOsvw9/yLum/9e0f/AKCK1K9uHwo+Xn8TFPSonkRPvOB9TUhrC1v/AF0X+7XFmWMeDw7rJXsVSpqpPlNJ9QtkODIM+1Vn1qFc7VYkd8VhUV8ZV4nxctIpI7o4OC3NSTWZCMLGFPuagfUrmT/lpt+gqlmivNq5vjKm9Rm0cPBdCV55HbLSMT9ajPJ5496SkrilWqT+KTZqoJbIhv7YXthcWx6SxsmfTIxXh+k61qPhnVg9ndyxrFJh41PDqG5GP617t0rwzxhZmy8UXsezYrPuUeoNfQcPV5RlKHzNYRUvdlser+H/AIv2mp6pFY3ljJbGZ1SKQNuBJ4+b0rqNTmSS9LIQQBt4NfMyFg6lGKsDkFeoPtXv+kQC10e0h3M22Mcsckk813Z9iZPDRpvqzH6rCnPmiX85pKSivjTQWjNNzRSAXNGaSimBX1A/8Sy6/wCuL/yNfPPQV9C6h/yDLr/ri/8AI1899q+p4f8AgnfujWkek/D7RNNv9GkubuzimnScqGbPYCu+jtLaEgxW8SEDAKoOK5D4Zn/inZ/+vhv5Cu0zXlZnWm8TKN9ETK9xc0U3NGa83Umw6obo/wCiT/8AXNv5VJmobo/6JP8A9c2/lWlHSpELanz7ICZWAGTk8V7P4M0ZNI0OJiMzzjzHY9s9BXkmnW7Xet29uv3nmA/I170OAAOw4x+VfSZ3Wcacaae5c9Uh+aTNNor5cmwuc1S1i+On6Rd3ajLRRsw+tWyeKz9ctnvtDvbaMZlliYKD61th0nVjzbXHY8RvdQu9RnaW6meR2OfmPHWu4+F9w5nvrfcfLCB9p6ZzXBTwTWs7RSo6SLxhhz1710Xg3xBaeH7q6luxIVljCr5a55Br7TGUvaYZwprfY0klbQ9izRXn9z8ToEk/0aweRPV32/41jXHxG1iTeIkt4lPT5CSPxr5uGTYmWrViVFnrGailu4Lf/XTRx56eY4FeJ3PijWrqJopdQmKMckAgAVmSXM8/+tmkkx03sTXbDIWvjl9w+S52PxHvLa71KzFvPHL5cRDFGBwc+1c34f48Q6d/18J/Os6tDQf+Rg07/r4T+Yr2o0lRw/s09kWlZWPdC/X600vUJem76+I5RqBPvppeoS/NIXp8o+QmL15b8QGz4iH/AFxWvSt9eZePTnXx/wBclr1snX+0fIbVjlu1epeA2x4ZH/XZq8tr07wMceHB/wBdmr1s2V8N8wirs6ovSb6h300vXzHKaKJOXpu/mod9IXpqJXKZ/iid4/Dl6ynnZj9a8i/ya9U8UPnw3eD/AGB/MV5XX0mUq1F+plNWYUUUV6u5DOi8JaQmp6g00wDQwYYg9GJ6V6SpVE2qAFHAUDgVy/gyPytFZiAC0h5HXoK6Ev7181mFWVSs4vZHVCGhPvppeod/FN31wcpqoE5fNcj45vXS2gtVb5XYlhXTF64fxsxN9b+myvQy6CdZXJqxtE5evWNLb/iVWg/6ZL/IV5PXqWnHGl2o/wCmS/yFehmqvCPqZYdXZoF+aTfUG+k38V4nIdigTF6QvUG+kL8VSiVyHP8AjRt1hAP9s1z3h041y3/3q3PGDZsYP9+sHQONat/96vcwq/2W3qcVRWq2PRy9NL1CXpC/vXi8h6KiTF6QvUBekL+9PkGoExeuQ8Ytuu7b/rmf510++uU8VnN3b/7h/nXdgI/vTDFRtTOj0h/+JRaj/YH8quF6zNKb/iVWwH9wVYedEOHkRf8AeOKyq026jsjemlyJssl+aQvWZLq9jCcPcLn25qlJ4mtFB2JI5HQngGnHC1JbIHUpx6m+Xpu+uVl8USsMRQKp9zmqcuv38p4k8v8A3BiuiOBm9zJ4umtjtd1V5L22jUlp4xjqCwrhpb25mbLzyE/XFQEknJJz6mto5el8TMnjv5UdnLr9hGpIlLn0UVn3niSKSB4oYmO9SuW4xmuboreGEpxd7GMsXN6B3ooorqOUUMQMAkfjSUUUA9QoorV8N6Umu+IbTTndlSdiCw+maaV3YTaSuzKorb1LwprWm6hPbyabcAIWZW2ZGwd9w4rEGcfpz2pyg4uzJhUjNXiFFFFSXqwooooEFFKvzMFXLMeAo61p2nhvWr+URW2l3bMRkZjZQfxIxVKEnshOUY6tmXRXeRfCLxRLJtKWsY2htzSHb9OOc/pWzY/BK9kize6tHDJn7sUZf+eK0VCb6GEsXRXU8qor3i1+DXh+JYzcT3czr9759qt+HNdHZeA/DVhKssGk24kXoxBOfrWscJN7nPLMaa21Pmq2tLm7Yra2805HURIz4/Ktux8EeJdRjEtvpE5TOCXwn88Gvpe3sLW1/wBRbxRf7iAVZxW0cGlq2YSzKX2UZ+jQSW2i2MEy7ZEgRXUnODgVoZoxS12JWVjzW7u4h6Vha3/ro/8AdP8AOt09Kwdc/wBfH/un+deJxF/uEvkb4X+KjLzRmkpK/NrHsDs0maSiiwC5ozSUUALmvLfifaCPVLS67yx7W/4D/wDrr1GuM+JFqbjw/HOqAmGUEn+6pH+NenlFTkxUfMqOjPM9Ii+0a1ZRY3bplyB6ZFe/ABV2joOMV414Atmn8UwOuMQqXbPpjj+deyZrtz6d6sYdkXU3HUU3NIW214Fr6Gdh+aTNNzxkUmaLDsPzSE02iiwWIL8/8S27/wCuL/yNfPvavoG/P/Etuv8Ari/8jXz9/DX1OQfBP1RpTPV/hn/yLk3/AF8H+Qrs81xXw1OPDs//AF8t/IV2RavGzJf7VP1Ja1HZopm+ml64bAkS5qG6ObOf/rm38qC/vUN0/wDos3P/ACzb+Va0o++h8p414dcJ4qsWPQTj+de3b+Pwr5/WQxXQkHVXDAfiK9t0zU49T06G7iYMHUZwehxXv53SbUanQu1zS30heoN9IXrwOXUfITeZSGSoN9Jvp8o+Qgv9L07Ulxd2kU3fkYP51z114A0iZnaJpomPQBvlH510+/im7666WLr0tIyK5DhJ/hxIqEwagGfsrJj9c1k3PgbWbdNyRxz5ONsb8/rXqBekL12wzbEL4rMrkPGbrSNRsmK3FpKhAz0yP0qjj6/SvcSwbhgCDwQRnNY2peGtM1JMvCIpMcSR8V3Uc3i3aorByM8nq/ofGvaf/wBd0/mKfrOjXGjXnky/Mh5R+zCo9F41yw/67pXqSlGdJyjtYi2p7Mz80m+oS/NNL18ZynSoExfmkL1AXpC9PlHyE++vN/HLZ10f9clr0AvXnnjU51sf9clr1Mqjav8AIiqrRuc3XpHgpseHh/11avNz0r0PwccaCP8Arq1elmavh/mRSV5WOmL00vUJekL187ynWok2+kL1AXpC9NRKUCh4lbPh28/3B/MV5h2r0nxE+dBu/oP515v2r6DLFaj8zkrqzCiiivRMbHofhVsaFH/vGtkvXPeFpc6MB/dcrW0Xr5vFx/fSPSpRvBExek38VAXpC9YcptyE2+ud8T6bPfJDJbpvdOGXNbReml/et6M3SnzoUqPOrHE23hq/mlCzRGJO7OR09q7mIeTCkXZVAzUTShVLMwAAySe1Q215HdQ+bESUJIGf6V0YitUrpSktETSoRpuy3Lpeml6g8yk3k9q5VB9DoULE++ml6rNcxpndIi465OKpTa3YxA5uFJHYVrGhN7ITlCL1ZU8WHNnD/vmsLRDjVoP96rmtarb6hDHHCHO1sncMVk2tw9pcLNHt3J0zXr0KUo0OVnmVZx9rc9EL8UjSADJ4HrXESa7qEjHE23PZRVN7qeTO+Z2B65auVZe+rOl4yK2R3UuoW0K7pLhAPrmqU3iCxhOA7Sf7grjOporeOBprcxeMl0R00nilAx8u3JHqxrF1HUZNSkWSVVXaMAL6VTorop0KdN3itTCeIqTVpPQs/wBoXfkiLz28sdAO1QvI8n33Zvqc0yitLIz5pdwooop3FqFFFFPVhoFFFFIAooooEFFH1p8UbzuEhRpG9EXcaLMHpuMorXsPC2u6mGNnpV1Jjr8m3+eK6Gz+E/ii8hEjQwW+f4ZpMN+Qz/OtFSm9kZSr047s4eu2+FFkbvxzbyDGLeNpTn/vn/2aultfgjKQj3erhf7yJF/I5rtfC/w90nwpqD3tlNcvK8flnzWBGM56Y9q6KWHnzJs5MRjaTg4xep1k0EVxC8MiBkdSrA9wetec6x8H9FuIrmbTjNDcOCYozJ+7Vu3UZ/WvSxRXdOlGe6PJp1Z09mfNI+G/iw3Pk/2S4G7Hmbhs+vXNb1l8GdbmlUXd3awRHksmXYfhgV7xijFYrCQR1SzCq1Y8osPglYR5N/qc847eUojx/OuhsfhZ4VtItj2H2k5zvmck/piu3pMVqqMFsjCWJqy3Zm22g6TZoiwadbIE4XEYyPxrQCgAAADHTAp9FaKKWxi23uxuKXFFLTEJRS0UAJ3paKKACiiigBD0rB13/Xxf7p/nW8awNe/18X+6f514nEP+4S+R0YX+KjKozTM0Zr83PasOzRmm5pM0WCw7NGaoxXW7VLi2ZuVRXC/z/WrmaqUHGwWFzWX4ktFvfD19C5xmJm/FeRWlmkkw8bKRkEYIrShJwqRl2Y7HnHwxtt11fXmfuKI9p9+f6V6TmuX8Haf/AGdZ3wcbZGunB+gPFdJvrrzOr7XENodrklZmvXP2bTS28K7SIoHqSwzV4vzXJeNrxVOlW38T3aP+X/66ywdL2laMR8p2O7ik3VCX6UhesJR1ZSiTFqQv71Bv96Qv70uUfIJfvnTrnn/lk/8AI14GPu17pfvnT7r/AK5P/I14WOlfS5GrQn6oaVj1D4ctjw/N/wBfB/kK68vXGfD1saDN/wBdz/IV1hevKzBf7TP1KUbk2+ml6hL0heuNRK5CYvUNy/8Aos3+438qaXqK4f8A0aYf9M2/lW1KPvork0PE5Pvt9TWvofiO80OTER3wE/PGTgf/AK6yH/1r/Wm19rKEZx5Zq6Mz1TT/ABtpd2oEshtpMciTpn61txXcM6hopkcHkbWrxCpYrme3O6GaSM/7DYNeXVyelL4HYfNbc9uL03fXk1v4n1e2UIl4xUdnGc/nWzbeOb7701ojxqOSmRXDPKasfhaZpGSZ3++ml+a5O38c2Ei/v4ZYm9ANwrbtNUtL9SbadZMDJAPI/CuWpg6tPWSNI8rNAvSF6g30heseU05SbfSF6gL0m+qUSlAyPF9stzojuVzJEwYH+f6VwWjnGtWJ/wCmy16HrTj+xrvP/PM4rzzSONZs/wDrqn8697ANvDyTOarG0kerl/mpN9Ql/wCVN314fKdiiT76aXqEvSb6fKUoE2+uA8YHOsL/ANclrty/Nc54h0a41O5jmttudmG3NivQy+UadW8jKtTbjZHF133hJsaIP+urVhQ+E7pifPljjGO3zVs+Gf3WlMh/hldc4xnBrvxtSFSi1F9TKhTkp6m+XpN/vUJeml68XlO9RJ99NL1CX96bvpqBXIVNffOiXX0H8689rvNcfOjXI/2R/OuDr28vVqZ52LVppBRRRXccp0PhnUVt5ntpWwJOV9jXV7j/APXrzMHByM/hWnba5fW6hRJvQcBW5rhxGE9o+aJ2YfEqC5ZHcF/ekL1zVtrGrXcFxPb2QligAaVkUkID0zVX/hJrrkCKL3yDXN/Z9U6VjaL0TOuL1Wub+3s0LTygY7d65CfWr6YMpl2qey1ns7OxZ2JJ7sc1tTwH87IqY2NvcNbVdckvh5UamOEHkZ+9+NOsdeNlYrbpCGZc/MTWNRXZ7Gny8ttDi9vO/NfU15fEd9IMKUj90FU5dSvJ+XuHJ9jj+VVKKpU4LZEyqzluxzOztlmYn1JzTaKKtaENsKKKKBBRRRQMKKKKACiuj0DwRrPiS0NzpqQtErlD5km3BrF1Gym0vULixusCaByj88ZB7GrdOSV2Qpxk+VPUrUVJFBNN/qoZJP8AcQn+VbFn4O8R38SzW2j3Txt0bbj+dJQk9kDnFbtGHRXf2nwf8TXDRmb7LBG2Ms0mWX8MV0dj8EUWUG+1dnTHKwxhT+ZNaRw9R9DCWMox6njtdPqngy507wvp2tpM06XiqxjSI/uwRnk8/wAq9bsfhF4ZtARPHNdk9DNIRj/vnFdlaaba2Wmx2EMKi1jTy1jPI24xjmuiGEdveOSrmCduQ+VrfS9QupEjgsbl2foBE2D+OK6Cy+G/iq9mEf8AZjwDGQ87ALX0nHFHGgVEVVHQAYAp9aRwcVuZyzKo9keF2PwW1iYN9tv7a29NgMmf5V0Nl8FNLSIfbdQuZZPWPCD8iDXqeKMVqsPTXQ55Y2tLqcfZ/DTwrZ+WRpiyyR/xyMST9egrobbR9Os3D29jbxOBgMkQB/Or+KXFaKnFbIwdSb3YwKB0ApQPenYoqyBKKWigBKWiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAQ1z2v/8AHxH/ALp/nXQmuc8RnFzD/uH+deJxAr4GXyOrBq9VGTnmjNQF+aN/FfnXKe5yk2+kL1BvpC9NRGoHPS332f4gxQ4yJ7bZ1+7jmunL15v4qufsvjTT5t21V2bm9s8134lDqGU5BGR716WMo2p05rqhqBOXpu+od9IXrgUS+UkG1NxUAbm3H3o31CXpC9Vy3GoE2+vP/Gd4ZfFOmW2MLEVbP+8R/hXc768s1i5+0+NGcNuVZ0C+wyK9XKqV6kpdkDier7qQvUJem7682UdTRQJ99NL1DvppehRK5AvX/wBBuf8Ark38jXiXavZbx/8AQbj/AK5N/KvGh/n9K+hyZWjL1RnNWPR/ATbdCmH/AE3P8hXU7+K5LwO2NEl/67n+QrpS9eZjo/7RI1hG6Jt9IXqDfSF65VA05CYvUVw/+jy/9c2/lTN9RTP/AKPL/uH+Va0o++huGh5Iyl5ioGSWwBWtb+FdWuV3eQIx1/enBP0rKRsXSH/bH869aWTMa8+lfRYzFToKPKtznpU1N6nGw+BrhkBlu40PdQucVpQ+CtPjKtLLLIR1HABroC9N315csdXl1sdMaESlb6BpNs++O0Qn/by36GjVoYIdCv1hijjzE33FAq5vqpqKtPplzEnVoyBWdOrUlUXNLqN0kk7I8v7kVteFZ2h1yLBOGBDAVjFSp2kYIOD+db/hexmbUVuXjYRRg8txk9q+hxDj7N+hx04vn0O9L5pC9Ql+Kbvr5jlPTUCYvSb+ahL00v3pqBXKZviecRaJIu7azkKPcVxWnOE1O2c9pQf1rT8S6iLu6WCJt0cXXHdu9YanDAjsa+gwlJwo2fU86tNOoeqeZn6daTfWPperQ39uhDBZQBuTPXtWgXrxqlCUJWZ6VO0kmiYvSF6g301pMfeOKlU2achOXpC9U5r2CD/WTov1NUpdesIW2mUv/uDNaxoTlsiXKEd2a++snQH/AOJfIP8Apq/86oyeKYlJEcDMOxJxWVb63c2tuYYgihmLZ7jNdlPCT9m00YSr01NM7bfTGlVTyyj6nFcPLq99OMNcMOf4Tiqkk0kpy8jOf9o5qo4D+ZkvGpfCjuZdVs42KvcoGHUZqlL4ls0B2B3I7YwDXIUVvHBU15mEsbN7G5f+IPtdpJbpAUD4BJOelYdFFdMIRgrROepUlUd5BRRRVkBR3oooEer/AATRZLrV1dQymJAQRkEc113iT4X6JrMMr2cKWF6+MSxr8uR6r0rkvgf/AMf+q/8AXOP+Zr2mvUoQjKmrnh4ucqdduLPmfxF4B13w5Gbi5gE1uXI82E7sf73pXL+vt19RX18yBkIYZB4I9a4rxL8NNE16MyQxCyuwMLJCoAJ/2h3rKphOsTejmPSofOtFdf4l+HWteHMy+Wbu0H/LaFen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   'markdown': 'Mistral 7B is released under the Apache 2.0 license. This release is accompanied by a reference implementation ${ }^{1}$ facilitating easy deployment either locally or on cloud platforms such as AWS, GCP, or Azure using the vLLM [17] inference server and SkyPilot ${ }^{2}$. Integration with Hugging Face ${ }^{3}$ is also streamlined for easier integration. Moreover, Mistral 7B is crafted for ease of fine-tuning across a myriad of tasks. As a demonstration of its adaptability and superior performance, we present a chat model fine-tuned from Mistral 7B that significantly outperforms the Llama 2 13B - Chat model.\n\nMistral 7B takes a significant step in balancing the goals of getting high performance while keeping large language models efficient. Through our work, our aim is to help the community create more affordable, efficient, and high-performing language models that can be used in a wide range of real-world applications.\n\n# 2 Architectural details \n\n![img-1.jpeg](img-1.jpeg)\n\nFigure 1: Sliding Window Attention. The number of operations in vanilla attention is quadratic in the sequence length, and the memory increases linearly with the number of tokens. At inference time, this incurs higher latency and smaller throughput due to reduced cache availability. To alleviate this issue, we use sliding window attention: each token can attend to at most $W$ tokens from the previous layer (here, $W=3$ ). Note that tokens outside the sliding window still influence next word prediction. At each attention layer, information can move forward by $W$ tokens. Hence, after $k$ attention layers, information can move forward by up to $k \\times W$ tokens.\n\nMistral 7B is based on a transformer architecture [27]. The main parameters of the architecture are summarized in Table 1. Compared to Llama, it introduces a few changes that we summarize below.\nSliding Window Attention. SWA exploits the stacked layers of a transformer to attend information beyond the window size $W$. The hidden state in position $i$ of the layer $k, h_{i}$, attends to all hidden states from the previous layer with positions between $i-W$ and $i$. Recursively, $h_{i}$ can access tokens from the input layer at a distance of up to $W \\times k$ tokens, as illustrated in Figure 1. At the last layer, using a window size of $W=4096$, we have a theoretical attention span of approximately $131 K$ tokens. In practice, for a sequence length of 16 K and $W=4096$, changes made to FlashAttention [11] and xFormers [18] yield a 2x speed improvement over a vanilla attention baseline.\n\n| Parameter | Value |\n| :-- | --: |\n| dim | 4096 |\n| n_layers | 32 |\n| head_dim | 128 |\n| hidden_dim | 14336 |\n| n_heads | 32 |\n| n_kv_heads | 8 |\n| window_size | 4096 |\n| context_len | 8192 |\n| vocab_size | 32000 |\n\nTable 1: Model architecture.\n\nRolling Buffer Cache. A fixed attention span means that we can limit our cache size using a rolling buffer cache. The cache has a fixed size of $W$, and the keys and values for the timestep $i$ are stored in position $i \\bmod W$ of the cache. As a result, when the position $i$ is larger than $W$, past values in the cache are overwritten, and the size of the cache stops increasing. We provide an illustration in Figure 2 for $W=3$. On a sequence length of 32 k tokens, this reduces the cache memory usage by 8 x , without impacting the model quality.\n\n[^0]\n[^0]:    ${ }^{1}$ https://github.com/mistralai/mistral-src\n    ${ }^{2}$ https://github.com/skypilot-org/skypilot\n    ${ }^{3}$ https://huggingface.co/mistralai',
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   'dimensions': {'dpi': 200, 'height': 2200, 'width': 1700}},
  {'index': 2,
   'markdown': '![img-2.jpeg](img-2.jpeg)\n\nFigure 2: Rolling buffer cache. The cache has a fixed size of $W=4$. Keys and values for position $i$ are stored in position $i \\bmod W$ of the cache. When the position $i$ is larger than $W$, past values in the cache are overwritten. The hidden state corresponding to the latest generated tokens are colored in orange.\n\nPre-fill and Chunking. When generating a sequence, we need to predict tokens one-by-one, as each token is conditioned on the previous ones. However, the prompt is known in advance, and we can pre-fill the $(k, v)$ cache with the prompt. If the prompt is very large, we can chunk it into smaller pieces, and pre-fill the cache with each chunk. For this purpose, we can select the window size as our chunk size. For each chunk, we thus need to compute the attention over the cache and over the chunk. Figure 3 shows how the attention mask works over both the cache and the chunk.\n\n| the | The cat sat on the mat and saw the dog go to |  |  |  |  |  |  |  |  |  |  |  |\n| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n|  | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |\n| dog | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |\n| go | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 |\n| to | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |\n|  | Past |  |  |  |  |  |  |  |  |  |  |  |\n\nFigure 3: Pre-fill and chunking. During pre-fill of the cache, long sequences are chunked to limit memory usage. We process a sequence in three chunks, "The cat sat on", "the mat and saw", "the dog go to". The figure shows what happens for the third chunk ("the dog go to"): it attends itself using a causal mask (rightmost block), attends the cache using a sliding window (center block), and does not attend to past tokens as they are outside of the sliding window (left block).\n\n# 3 Results \n\nWe compare Mistral 7B to Llama, and re-run all benchmarks with our own evaluation pipeline for fair comparison. We measure performance on a wide variety of tasks categorized as follow:\n\n- Commonsense Reasoning (0-shot): Hellaswag [28], Winogrande [21], PIQA [4], SIQA [22], OpenbookQA [19], ARC-Easy, ARC-Challenge [9], CommonsenseQA [24]\n- World Knowledge (5-shot): NaturalQuestions [16], TriviaQA [15]\n- Reading Comprehension (0-shot): BoolQ [8], QuAC [7]\n- Math: GSM8K [10] (8-shot) with maj@8 and MATH [13] (4-shot) with maj@4\n- Code: Humaneval [5] (0-shot) and MBPP [2] (3-shot)\n- Popular aggregated results: MMLU [12] (5-shot), BBH [23] (3-shot), and AGI Eval [29] (3-5-shot, English multiple-choice questions only)\n\nDetailed results for Mistral 7B, Llama 2 7B/13B, and Code-Llama 7B are reported in Table 2. Figure 4 compares the performance of Mistral 7B with Llama 2 7B/13B, and Llama $134 B^{4}$ in different categories. Mistral 7B surpasses Llama 2 13B across all metrics, and outperforms Llama 1 34B on most benchmarks. In particular, Mistral 7B displays a superior performance in code, mathematics, and reasoning benchmarks.\n\n[^0]\n[^0]:    ${ }^{4}$ Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.',
   'images': [{'id': 'img-2.jpeg',
     'top_left_x': 294,
     'top_left_y': 191,
     'bottom_right_x': 1405,
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gOg2UWtp4Z0e5mZ7Nde1CHZb3DFBEsZIRWzkoeRnuCfWtyz8D6FNqHjW0ktHe108A2Vt577LctEWYoM8EkDn2oA9jaRVBJbaB1J4A/PtWXr2v23h+ztrm4SWSO4uorVfKwcNIwUHkjjJryTTrPTfEfinwjB4hmMyTeGI38qWUqtw+88Ng/Nxk474ps4js11LSNOnebRbHxPpy2ZLlxEzMpkjVj2Vs/SgD3EyKrKrOAx6AnrTgTjr/WvBtQsrrXdQ8WXl9ZafLPZ3UiJdXWpPBJZRrjyyigYUYw3uTXpU+o6rH8KH1CKfz9TXTPMWaMH52CfeGR1xzQB1olVmZFZSy9VB5FYVh4oj1LxPf6NbWdwUsMCa7JXyt5VWCjnJ4avNZtL0HRtA8K654fui2tXV1bp563DO92HI80OCeRgtnI4ro/AHh7S9M8ZeL5bOzWF4bxIY2DMcI0SMRz6kk5oA9HHTmlqrJfWsF5DZy3UKXMwJihZwHkA64XqcCmrqli/2rZe2zG04uAJlPk8Z+fH3eM9aALlJUdtcwXltHc200c0Eg3JJGwZWHqCOtSGhgcrrf/I8aN/2Dr3/ANGW1Xqo63/yPGjf9g69/wDRltV6vl82/wB4+SOyj8JDdyyw2k0kEJnmRGZIgwXewHAyelcZ/wAJxrTa22jr4TmN6sBn2fa0+5nbnOPU13NcXGP+Lxy56f2Jj/yKK5MOotS5leyLkamh+KoNVF6t3GmnzWt2bQxzTKSzhVY4P/AsVoLqTHVrm0kg8uCKFZftDSLg5J+XHbHvXnVv4b0nU/8AhP7+9sori5S8uEieQAmPbErZXP3TkjkVhzCLULErf6kbUTaLpha4kQshYkkb+fuk9T711PCU535X2/GxPM0e1213bXiF7W5inUdWicMP0qY15t4IltrTxjd2K2Wmw3Elmspl0qYG3YBgOU/hY5+uBXpPauPEUlSnyouLuIeh+lYvg7/kStC/68IP/QBW0eh+lY3g7/kStC/7B8H/AKAKS/hS9V+odTaooorF7FFHV76XTdOkuorY3LqQBEHCZyQOp/zxU5vbVbgWzXUK3B6RM4Dn/gNcx8SyR4FvSM53xYx6iRTXnepRabYLqeoltM1KEakZHlMn2fUYX3/dUnJYe3oK7qGGjVjzeZnKVme4/n+NLTY38yJH2su5QcMMGnVxNWbRoFcp41/1ugf9hE/+k81dXXKeNf8AW6B/2ET/AOk81b4X+Kvn+RdL+JH1X5lDtS0nalrt7H0IVDHd28shjiuIXdeqhwcfWqut3Edrod/NL5hRLdyfLbDYx2PY+9edwG2t9Y8PSQLptuX3Ei3k3SspTrK3fP8APNb0qKnFtnNWr+zkoo9NF3bGRYxcxGRvurvGT9BU9eZRaVZReAY9RSBDfG48z7T/ABg+bjg9cY7V6YhLICTkkZqatNR2Ko1JT3XS4tGKKKyNzoPhz/qvEH/YU/8AbaCu2rifhz/qvEP/AGFP/baCu2r6Kh/Dj6I+QxP8aXqwooorUwCiiigAooooAKKKKACkNLSGgCC8/wCPOf8A65t/KuW8M/8AIq6R/wBeUP8A6AK6q7/485/+ubfyrlfDP/Iq6R/15Q/+gCvDzr4YnRh92alc/wCI9e1LRI5J7bRHvrWGFpZpVnVNgUEng9eBXQVk+Kf+RS1n/rwn/wDQDXiUbc6ujpexzcfj++Gkwatd+HZYLC4MYjl+0qc72AXj8SfwrqrvVoI9Ou7m0aK8e3UkxRTDOfTPb6+1eea3Ctz8F9DhfOxzZIccHG9en+NVfGek2GgavcwaTax2cU+gXAlSEbRIVZcEgdSPXvmu9YalOSS0d3+BneS3PUl1G2HlJPPFDPKobyXkAbn2781brwm9tdOjh1m/L6VqKrsM8V4/k3kDKg4hcnOPTHWva9KuFu9IsrlUkRZbeN1WX74BUEBvf1rmr4dU4qSe44SuW6xLj/kdtP8A+wdc/wDoyCtusS4/5HbT/wDsHXP/AKMgrGl8Xyf5Fs26KKKyWwwrOTVP+Jlf288Igt7VUIuHlXa+4Z6dRj361o1454rgt7vxvrsNzfwWo86x2fa0LQOfLY7ZMHhT7966cPRVVuL/AK1InKyPXoLiC5i8y3mimTP3o3DD8xUtef8Aw/mt49b1rT4rK0tpkEcsg0+cSWzcHG1R9xvUCvQPp0qK1P2c+UcXdBRRRWUdxs474d/8g/XP+w1ef+h12Ncd8O/+Qfrn/YavP/Q67Gt8T/GkKOwVFPcwWsXm3E0cMecbpHCjP1NS15943lsrjxXpdjcWtpPKtvJMv9ozBLZV6E7SPnb059amhS9pPlCTsjuvtdr5SS/aYvKf7j+YMNxng/madBdW90jSW88Uqg4LIwYA+nFeF6bBaanbaZpjy201p/wkjq0dox8opsY4Qddh5x7V6D4esbXSPiTrthp8EVraNZQS/Z4V2oHyQSB0HT8a6auEhBb6pX/EhTudx+f40Ud6K4DUwfGf/In6p/1x/qK5+WeGBVM0qRhjgF2Aya6Dxn/yJ+qf9cf6ivI9cWSfxtcx3a6c8KWqG3TUGITB+8yjoTnIz1r08FTU4tHXhavs1JryO+EsbP5ayKzldwUMM49fpTGuoEjaVp4xGG2l2cBc+me1eWgXY0mwTSLgz6whnWF4s4NuOMEt1APCn8KvmbRpbfQrW1hhmURSS4v5gsW/o5kGMs2cnH5V2vCpdTrWLv0PQxcwFFkE8XltnaxcYP4037da+Wsn2qHy2O1XLjBPp9a8v0yG0v57awkNvNb/ANtyAx25IjKeWCAo67cg/WtiDQNKfWvFcJsYfKt4ovITb8sZaLJKjsc88UpYeMb3ew44mc7WXkd1Jc28QJlniRRg5ZwOvSqd/rEdjLpyhPOF7cCFXRhgZBOffpXA6KNJutZ04a00Mn/EoiMS3LDazAnPXvj19KY7lFjXS2L2cetMLIgkr/qzwvtnOKv6tHm5SHi5cnMkempcwNMYFnjaYdUDjd+Va/g//kcp/wDsHn/0YK8VsI86dpdyk2mRXZuI8zIzG5Zy3zBh1Oe4PAr2rwfk+Mp89f7POcf9dBWlClyV0Y4ut7TDy07HolKKSlFesfPhXnfjL/kc7b/sHn/0ZXoled+Mv+Rztv8AsHn/ANGVzYv+DI7cv/3iJm0lL3orwep9RcBzUJuoBN5Hnw+bjPl7xu/z7U+UsI28v/WBSVz64ry9U0b/AIQZ7uV0Gvhy3mE/6QJ9+Ao749vStqNFT1ZhWrOD0XmdzDrwuvEc2kW8HmC3UGafzR8pIJGF6tyMH0rarjPDOn26eLNXne1hW4jjt23BRlXZDvIx0yetdnwOnSpqxjFpR7DoSlKLcu4UHpRQelZo3ZJ8Lf8AkR4/+vu4/wDRr12tcV8Lf+RHj/6+7n/0a9drXFi/48/U+aCq2oXTWWn3F0kRmaKMuIwwXdgdP/r1ZrC8Zn/iitZxnP2STp9Kypx5pJA9DQXUrdYbc3M0VvLKgYRPKobnt789xV0dK8PvLSxkn1G7aXSb9xYW/n2mpHypoQIhzC5PGRg5x1+leteGLuO+8LaXcxJKkclrGVWVtzgbQBk9z7966cRhlTipIiMruxrUUUVxmhieLP8AkBf9vVt/6OStusTxZ/yAv+3q2/8ARyVt1o/gXqxBRRSYzketQDIGvrRLlbZ7qFbg9ITIA5P0pJNQs4XCy3kCMW2hWlUHPp9a8H8RXWn3PhbVtWtItMgme+Z457mbffs4kH3MYKAY6dhXWW+g6Rq0HjfULq1hu5RNJ5MxAbYBHlTHnpyc5Fei8FCK5m/60/zM1U6I9VHTg0VjeE5ZJvCOkySsWc2seSTknitmvPmuWVjRbBWJAP8Aitr/AP7B9v8A+jJa265q6adfEmsta4+0LpMRi/3t02D+dXTV01/W6FI3he2rXJt1uoTMBkx+YNw98elKLq2MCzieLyWOA+8YOa8M06ENo2kXqT6JBf8A2pN1wjsb1pN3zpIOp7gg8AH8av2X2NPGMZZ5/wDhCf7Rb7OW/wBSLvH57N2cds12vArW0n9xn7Q9ie+tIrhYJLqBJmPEbSAMfoKJL6zgbbNdwod2wB5Ap3enueleIeJZ9OutI8QX0cOlwy/b3AnvJxJel1cDbGAAVHHHbGa1rXRdL1fSfGuqXltFdXKHEUzfP5eIVbch7HJ6jsBR9Rildtr+kPnuetJeWr3DQJcwtMnLxhxuH4Ui39m0yRLdwNI/KoJVyw9QO9eYPo9hpVt4KvbO2SG6nbZPOo+eYNCzNuPVuRxmuc0+DwmvwgN5FLbr4gTLJKrj7Us+/wCQDndgjAwOMc0LBwaUk3r5f12Dn1sex6fr0N7qGrWzJ5K6dKsbyMw2nKhs+3WtGK4guYDJBNHKhyAyOGBx7ivDtbkv31maO8+zray6pCL8XTEQmT7OpCuV/hLZ46dM12PgOFrXxXrUNvNpwtTBG0lppzEwxSZ4I7AkZyB6ClVwcYw5k+36CUz0TwVk+B9CP/TjD/6AKKXwV/yI2hdP+PGHkd/kFFfUpaaHHLdm9VW/sbbUrOW0vIUntpBh4pBlW+tWqKoCpcadZ3cduk9vHItu6vEGGdjDoRUV3o2m30s0l1YwyvNAbaRnXJaM9UP+zWhRQBgal4M8N6w8D6ho1ncvAgjjZ0yVUdBnuPrWzBbQW1qlrBEkVvGuxI0GFVemAB0qbAooAwb3wb4d1DTINPu9Gs5rS3JMMTIAI8nJ2kcjn0rS03TLHSLFLPTrSG1tkztiiTaB6mrlFAHO3PgXwtd6hNfXGg2EtzNnzJHiBLZGM/X361oWGh6Zpjq9jZQwOsCWwZFwRGv3V+grSxRgUAVbHT7TToGgs7eOCJnaQogwNzEkn8STWXe+DfDeoaumqXei2U98pB86SIEkjoT6n61vUUAUIdH06C4vJ4rOFZbwAXL7eZQBgBvXgkfjWZc+BvC95bWtvPoVlJDaE+QjR8ICSSB7ZJOOldFRQBiWfhLQNPukubLSLS3lSQyq0cYXa5XbkY6ccVeXS7FXu3W1jDXgxcEL/rRjHzfhxV3FGB6UAcVffDzS9S8TwXd3ZWc2kwaYLGKzeP7jCTcCOwABxxW9B4a0W20yDToNNtorKCRZooVQBVcchvr79a18UUAYWp+D/Dusakl/qOjWdzdoABLJGCcDpn1/GtoIgTaFUKBjGOMelPxRQBg2Hg3w3pmqNqVlotnBenJ85IgCCeuPT8K1LewtbWe5nt4I45blw87KOZGAAyfU4GKtUUAUp9KsbnUbbUJrSJ7u1DCCZl+aMMMHB96jj0TTIzflLKFTf/8AH2Qv+u4x83rwTWjiigCCys7bT7OK0s4Uht4l2xxoMKo9AKmNLSGhgcrrf/I8aN/2Dr3/ANGW1XqzvE8gsPEmk6pMrCzjtrm2kkVSwR3aFlzjoD5bc1B/wlOhjj+0of1r5rNKVSWIvGOlkddKS5TYqH7JbC7N2LeL7SU8szbBv2Zzt3dcZ7Vnf8JTof8A0Eof1o/4SnQ/+glD+ted7Cqvsv7jTmXc0FsrREnRLaFVuGLTARgCUkYJb1JAA5qL+yNN8poxp1p5bRCBl8lcGMdE6fdHYdKqf8JTof8A0Eof1pP+Ep0P/oJQ/rT9lX7MOZdy3p2j6bpAcafp9raCT7/kQqm764HNXax/+Ep0P/oJQ/rS/wDCVaH/ANBKH9aUqNV6tMOZdzWPQ/Ssbwf/AMiVoX/XhB/6AKU+KdEwcajEeoAAJyf596y/DOvafYeF9Ls76ZrS5trWOKWKeNkYMqgdCOhxkexFaqjU9m/de66eocyvudbRWP8A8JVof/QSg/Wl/wCEp0P/AKCUP61j7Cr/ACv7h867mlc2tveQNBdQRTwtjMcqBlODkcH3qnLoGjzagL+TSrJ7wHd57QKXz65xnNQ/8JTof/QSh/Wj/hKtD/6CUP61SpVlsmK8e5r8dqKyP+Eq0P8A6CUP60f8JTof/QSh/Wp9hV/lf3D5l3NeuU8a/wCt0D/sIn/0nmrU/wCEp0P/AKCUP61znirVrfUX0t9PWa8jtLrz7h4IyyxJ5bx5J+si/qexrowtCoqqvF9fyKpzipxu+qAcgUtUP7YsP+flRj1Uj+lH9s6f/wA/S/kf8K6/Zz7M9/21PpJF5lVkZGAKsCCD0PaqCaFpEQ2x6ZZqNwfiBR8w6Hp1pf7Z0/8A5+l/I/4Uf2zp/wDz9L+R/wAKpQqLZMTqUnu0WPsVp9lFt9mh+zg5EXljbnOenTrU3FUf7Z0//n6X8j/hR/bOn/8AP0v5H/Cl7OfZgqtJbNF+iqH9s6f/AM/S/kf8KP7Y0/r9pX/vlv8ACj2U+zG69NfaOv8Ah1/qfEH/AGFB/wCk0FdqOlcb8PIJk03VLqSGSKO8vzLCJFKlkEMSZwenKN+FdkOle9Ruqcb9j5TENOtJruxaKKK1MQooooAKKKKACiiigApDS0lAEF3/AMec/wD1zb+Vct4Z/wCRV0j/AK8of/QBXVXCmSCWNerKVH5Vwei69pthodjZXtwLa6trdIpYpVIZWVQDx+FeNm8JThHlVzeg7XOmpssUc0TxSoskbqVdGGQwPBBHcVlf8JVof/QSh/Wj/hKdD/6CUP614XsKv8r+46OZdy82n2T2sdq1nbm2jxshMS7Fx0wMYGO1Fxp1jePvubO3nYxmLdLErHYeq8jofSqP/CU6H/0Eof1o/wCEp0P/AKCUP60/ZV+zC67ktz4e0W7uUubnSbGadAFV5LdGYAdBkjtWljHp+FZH/CVaH/0Eof1pP+Ep0P8A6CUP60OlWe6Ycy7mxWJcf8jtp/8A2Drn/wBGQVJ/wlOh/wDQSh/Wsm417T28U2V7HK8lpHZzwyTRxOUjZmiYbmxgcK3eqp0Kib917Pp5A5LudbRWR/wlOh5OdSgz+NJ/wlOh/wDQSh/Wo9hUv8L+4OZdzYqncaVpt15/2iwtZjPtE2+JSZNv3d2RzjtnpVT/AISnQ/8AoJQ/rR/wlWh/9BKH9aFRrLaLDmXcuWGmWGlwGHT7K3tYmOSkEYQE+pwKt1j/APCU6H/0EoP1pf8AhKdD/wCglD+tDo1nq4sOZdzXorI/4SnQ/wDoJQ/rSHxVogBP9oRn2CsT+GB/9fNCoVf5X9wOStuY3w7/AOQfrn/YavP/AEOuxrhfCN5H4ftdQh1kSWMl3fTXsImUruikO5fx9R26V0X/AAlOhjg6lDn8a3xNGo6rai/uFGStubFUtQ0fTdWEY1HT7W7EZynnwq+36ZHFVf8AhKdD/wCglD+tJ/wlOh/9BKH9awVGsndJj5l3LMWh6TDMJotMs0lDBwywKCGAwDnHUDjNWltbdLp7lYIhcSKEeUIN7KOgJ6kD0rN/4SrQ/wDoJQ/rSf8ACU6H/wBBKH9abpV3umF49zYo71kf8JTof/QSh/Wk/wCEp0P/AKCUH61LoVf5X9w+ZdyHxn/yJ+qf9cf6iuau9OsdQVBe2kFwE5XzYw2M9cZrV8S61Y6n4evbHTZGvbyePZHDbxs7E+uPQVijWLHHzTeW3dHUgj2PFd+HpVIw2e56GBqU1zJvsWIbO1t5RLDbQxSBAgdEAO0dBkdvaoJdG0qZdsum2jqWL4MKkFvXp196T+2dP/5+l/I/4Uf2zp//AD9L+R/wrbkqeZ3+0ovqvwHxaVpsE/nxWFskuQ29YVBBAwDnHoT+dTi2t1kmcQxB5seawQZfAwN3rxxzVX+2dP8A+fpfyP8AhR/bOn/8/S/kf8KHCp2Y1UorqikfDFlJq0lzNb20tq1ukK2xhBVdpJGOw61qJY2iRwotrAqwENEojAEZHdfQ8npUB1jTyMG5XH0P+FH9s6f/AM/S/kf8KbjVb1uTGVFdV+A+PStOivDeJYWy3JOTMsShs+ucZre8H/8AI5zf9g8/+jBXPf2xp/8Az8L/AN8t/hXQeB9134kur+FHNqloIfNKkBnL5wM9cAH866MJCftU2mceYVKf1dqLPRKUU1fu04V7B86Fed+Mv+R0tv8AsHn/ANGV6JXnfjsNaeI7S/lVhataND5oXIVw4bB+orDFK9GSOvAtKvFszMilqh/bGn9rgY7fK3+FL/bFh/z8D/vlv8K8T2U+zPpvbU+6+8u8VUOlacb4XpsLY3QO7zjEu/PrnGab/bFh/wA/A/75b/Cj+2NP/wCfgf8AfLf4U1TqLZMTqUnu0WkghjmkmSKNZZceY4UBnx0ye+Kkqj/bFh/z8D/vlv8ACj+2LD/n4H/fLf4UvZz7Maq019pfeXqQ1S/tiw/5+B/3y3+FNbWLHB2zF2xwioxLHsAMUKnPsxSrU7P3kanwt/5EePH/AD93P/o167WuE8FXUfhrw8NL1ndY3SzSyhZlIDK7FgQcYPBH0Oa6T/hKtDz/AMhKH9a48TQqutJpPc+e5ka9MmhiuYXhniSWJxtdHUMrD0IPWsv/AISnQ/8AoJQ/rR/wlOh/9BKH9aw9hV/lf3D5l3Jbvw9ouoPG93pNjO0ShYzJArFAOgGRwK0VUKoVQAoGAB0FZH/CU6H/ANBKD9aX/hKtD/6CUP603SrNWaYrx7mvRWR/wlOh/wDQSh/Wj/hKtD/6CUP60lQq3+F/cPmXcj8V/wDID/7erb/0clbdcl4i1yy1DSvs+nNJfXHnRSeVbxlm2pIrsfbAU1qjxVomBnUIlPowII+oPQ1boVORe6930Fzq+5sUdetZH/CU6H/0Eof1o/4SnQ/+glD+tZ+wq/yv7h8y7kknhvQ5bieeTR7B5pwfNdrdCz565OOc1Zt9MsLS3e3trG2hgcYeOOJVVuMcgDngYql/wlWh/wDQSh/Wj/hKtD/6CUP61bp13un+Irx7mpDDFbwpDDGkcSAKiIoCqPQAdKfWR/wlOh/9BKH9aP8AhKdD/wCglD+tT7Cr/K/uHzLua9YkH/I7X/8A2D7f/wBGS0//AISnRCcDUof1/wAKyYdds18VXN+4mXT5bSKBLtom8ppEZ2ZQ2PRx+RFaU6FVX917dhOS7m6NA0db974aVZC6cndN5C7zng84zzUp0nTmsFsDYWhsl+7b+SvljnPC4x1qmPFOh/8AQTh+vPNL/wAJVof/AEEof1qeSu+j/ELxJH8O6JJcy3MmkWDzSqVkka3QswIwQTjnjip7fStOtbaS3t7C1igl/wBZHHCqq/GOQBzxxzVT/hKtD/6CUP60n/CU6H/0Eof1odOu90/xBSj3Ls+m2ksEcX2aACFcQHygRDwVyo7YBPT1rF8N+DNM0TTdPims7O4v7Ndq3ht1D9SRyRnjOKu/8JTof/QSg/Wl/wCEq0P/AKCUP600q6jypOwXje9y5JpthOlwktlbSLckGcNED5pH97jn8aLHTbHSrdoNPs7e0iyWKQRhATjrgDrVP/hKtD/6CUP60yTxTowjYx3qyvg7Y41Zmc+gGOc9KI06zdrOwm42NnwV/wAiPof/AF4w/wDoAoqbwtaTWHhPSbS4QpNDaRJIp6qwUAiivr1eyOJ7mxRRTSecZrQQ6imBumTgGgN2zkjtmgB9FNB4pw6UAFFFNz82M0AOophbnrx3pw5FAC0UUUAFFFFABRRRQAUU0k5pVIKgg5B5BoAWiiigAooooAKKKKACkNLQaT2A5XxLLcXGvaVpMd3PbW88FxcTNbvskfyzEoUN1A/e5OOTtAqr/wAI9D31HV8+2qXB/wDZqs63/wAjzo3/AGDr3/0ZbVcdlRGdmCqoyWJ4A7k/hXzuZ16sK9ou2iOqlFct2ZX/AAj0P/QR1j/wZ3H/AMVR/wAI9D/0EdY/8Gdx/wDFVpxTRzRiWF1ljPKsrZDfQio/tlqA/wDpMP7ttjnzB8rdMH39q8/6zX/mZryxKH/CPQ/9BHWP/Bncf/FUf8I/D/0EdY/8Gdx/8VWsfxoB+n5/pR9arfzMOWJk/wDCPw/9BHWP/Bncf/FUf8I/D/0EdY/8Gc//AMVWv+NMaWNZFjZ1DuCVUn5jjqQO+KPrVd/aYcsexlnQIgDjUtZU4wGGpTkj82x6dRXFaZcalqek2d9e6zqjz3EKykpdtCo3DOAqEDjp0r0p/uN9K818O/8AItaX/wBekX/oIrrw2JquEryfQ7MFRpym7q5b8i4/6C2sf+DGb/4qjyLj/oK6x/4MZv8A4qrFJmtvb1P5men9Vo/yog8m4/6Cusf+DCb/AOKo8i4/6Cusf+DGb/4qpnkSJC8jKqjqWOBTsYo9vU/mYfVqP8qK/kXH/QV1j/wYzf8AxVHkXH/QV1j/AMGM3/xVWKKPrFT+Zh9Wo/yor+Rcf9BXWP8AwYzf/FVe0G+vtO8UaVbjULue2vpJIZYrqdpQMRPIGUsSQQUxjuGPpUNLY/8AI4eGv+v2T/0mnrfD1puqk2c2Mw9KNCTUUj1MYAApQKTGAAKUV7Z80LgelGB6UUUAGB6UYHpRRQAYHpSYHpS0UAJgDtS9KKKACiiigAooooAKKKKACiiigApKWkoAiuH8uCR/7qlufpXDaTp0mq6TZ6jfapqrXF1Cs7iK+kiQFhnAVCAAM449K7e7/wCPSf8A65t/KuV8M8eFdIx/z5Q/+gCvHzarOnGPK7G9BJ3uN/4R+D/oI6x/4M5//iqP+Eeh/wCgjrH/AIM7j/4qtJp4UmWFpo1lflULAMR7DvRPPDbJ5lxMkSZxukYKM14n1rEfzM6OWJm/8I9D/wBBHWP/AAZ3H/xVH/CPw/8AQR1j/wAGdx/8VWokiSF1R1Yo21gDnaeuD707I65wMZ/yaPrVdO3Mw5YmT/wj8P8A0EdY/wDBncf/ABVH/CPw/wDQR1j/AMGdx/8AFVr0UfWq/wDMw5I9jI/4R+H/AKCGsf8Agzn/APiqzZ9PuI/Ednp0Os6ulpNazTyxm8di5RowuHOWUfOc7TXTRyxzIHhdZEP3WU5BH1rHuP8AkdtP/wCwdc/+jIK0p4mvzWcns/yE4R7En/CPQZP/ABMdY/8ABnP/APFUf8I/D/0EdY/8Gc//AMVWvRWSxVe3xMahHsZH/CPw/wDQR1j/AMGc/wD8VR/wj0P/AEEdY/8ABncf/FVr0xZY2leNXUumNyg8qD0yPzp/Wq72kw5EZf8Awj0P/QR1j/wZ3H/xVH/CPw/9BHWP/Bncf/FVrjpRSWLr/wAzDkRkf8I/D/0EdY/8Gc//AMVSf8I/EOV1LWVI7jU5s/q2PTrxxWxRTWLr3+Jg4R7HmNhq2sa614b/AFe8Bs7mSyUW0zQBxGSpc7CMsep+vpV3yLjtqur/APgwm/8AiqyPDPLa3/2GLv8A9GVv16tWvU537x7GGw9J0otor+Rcf9BXWP8AwYzf/FUeRcf9BXWP/BjN/wDFVYorP6xU/mZv9Wo/yor+Rcf9BXWP/BjN/wDFUeRcf9BXWP8AwYzf/FVYoo+sVP5mH1aj/Kiv5Fx/0FdY/wDBjN/8VR5Fx/0FdX/8GM3/AMVVik/Cj29TuH1aj/KirLdX+kqt9batqJeJlYpPdPKjjcMgqxI5HevX0A2DivHNWP8AxK5+P7v/AKEK9kX7or1MBOUoNtniZpTjColFWFwPSjA9KKK7jzAwPSjA9KKKADA9KMD0oooAMD0pNo9BS0UAFFFFABXJa+Z77xLDpgvLm3tltTcOLaUxNI27aBuHIH0rra5TUP8Ake1/7Bv/ALUrkx03DDyaZdNJyVyt/wAI9D/0ENY/8GVx/wDFUf8ACPQf9BDWP/Bncf8AxVarMsaFmIVVHUnAA9TSRSxzxiSF1kjYcMjZB/GvlvrVffmZ2csTL/4R6D/oIax/4M7j/wCKo/4R6D/oIax/4M7j/wCKrQ+2WuXAuYT5b7H/AHg4bOMH37Y9qn9uuDim8TXX2mHJEyP+Eeg/6CGsf+DOf/4qj/hHoP8AoIax/wCDO4/+KrXBB9/p6/Wij61X/mYckexkf8I9B/0ENY/8Gdx/8VSNoCKCYtV1iNx0b+0ZmwfXDNg/Q1qmSMSLGzqHYEqueSB1IFP7e+KFiq/8zDkicb4WkvPFej/2lqmp6gJTNJAkdtctAiiNiucIRknbnJ7mtv8A4R+E8/2jrH/gzn/+KrE+GX/Inf8Ab7df+jnrsq2xGJrKrJKT3FGEbbGR/wAI/D/0EdY/8Gc//wAVR/wj0P8A0EdY/wDBncf/ABVa9Nd1iRpHYKigsSxwABWP1qv/ADMfIjK/4R6H/oI6x/4M7j/4qj/hH4f+gjrH/gzuP/iq1gQQCpBB6EHrS0fWq/8AMw5EZH/CPw/9BHWP/BnP/wDFUf8ACPw/9BHWP/Bncf8AxVa9FH1qv/Mw5I9jltcsrvSNN+1aXrGpw3BmiizLcvOpV3CH5ZCRn5sj3A9a0R4fiIBbU9Ycnq39pTDP5NimeLP+QGP+vq2/9HpW3WjxVbkXvPdiUI32Mj/hH4f+gjrH/gzn/wDiqP8AhH4f+gjrH/gzn/8Aiq16Kz+tV/5mVyR7GR/wj8P/AEEdY/8ABnP/APFUf8I/D/0EdY/8Gc//AMVWvRR9ar/zMOSPYyP+Efh/6COsf+DOf/4qj/hH4f8AoI6x/wCDOf8A+KrXoo+tV/5mHJHsZH/CPwHj+0NY/wDBnP8A/FVzWqf2lb6/JpSa5qX2BLdLgL9oYPuZmUgyZ3FcLnGe9d5XEa3/AMjpN/14Q/8AoySujDYqs5O8mbYelCVWKkir5Fxn/kK6x/4MZv8A4qjyLj/oK6x/4MZv/iqsdTmiun29T+ZnsLDUf5UV/IuP+grrH/gxm/8AiqPIuP8AoK6x/wCDGb/4qrFFH1ip/Mw+rUf5UV/IuP8AoK6x/wCDGb/4qjybj/oK6x/4MJv/AIqrFMeRIkLyMEQDlmOAB9aPb1P5mH1aj/KiLybj/oK6x/4MJv8A4qkMV2oYxazqyS/wsb6VgD24LYIz61ZpCBgnvimq1R7sTw1G3wnoPhq/m1XwvpV/Pjzrm0jlfAwNzKCaKqeB/wDkQ9B/68If/QBRXvbpHyjVm7HQ1438VBdwfELQdTst3naZYy3m1R95Uddw/wC+Sa9krmdS8MNqHjWy1p5Y/s8NjNaSQkZLeZjn6cGrJPOvjRqf9u6JBp9hNmCGzGrXLKeChYLGD9SzH8K6HXPFCeGtY8T6hb6SLi5s9PtHLK7fvAzFRkdAq5ySOcCq9t8Kri38HeINJOqJLd6kUiguJFJEMEePKQj25/Ot/UvCOpXGrazf6fqwsZr22gigdY95jMbEncD94MDg0AZNv4p8RatourC2OiXYWxM0F/Y3ZaJWyAyMPvBtuSDjqKo6Z4w8S6T4N8Ixmxt9Q1DVT5MW6dslfLBDOxH3s5zWlpPgLUP7eudY1SXSrWWWxey8rSrdo1k3/wAcmTyw9BU+k+C9WtbfwzFfX9nJ/YczFTDGy74jHsUHJ69c0AV9b8YeJtIjlFxFoVk1pbLJLJd3ZVbmTbuZIhjPHTnv2pnhzWR4h8f6ZqyxGJbzw6JvLJztJlXjPfvzTtU8B6xceJdZ1C0vNKMOqqitNe2plntAE27Y+cAdT2q34O8E6j4fvdOuL68tpfsemHT8Qow3DfuDc9OBQBN4l1KDT/HuhltPFxOtjeSpIrHegVVJUKODnpWd4S8eax4ivLFjHo89tdZ86C1u/wDSLLg/6xWPPpwK6DW/DVxqninS9Xhvfs62dtcwnaMuGkVQGXtkY71zlt4C1u413Sb3VrzST/Z03m/bLO2ZLq54xiRugB70AdZ4o1XUtJ0xJdMtYZ53lWMvcShIoFPV3PXA/XNZPgzxfda/qmr6Xdtp802n+U32jT5TJE4fdgZPcFSDU/jfwxd+JbfTWtJbUS2N0LnyL1C8M3BG1wD05qn4U8KazonijUNVvrrTXgvraON4bS3MQieMnaFHdcM2T9KALOt+JNWPimLw34ftbaW9Fr9suJrt2WOKPdtAAHLEkfgKwrX4j6xJYXcE2jwLra6r/ZVvbLKfLZ9oJdmx90cn6Yp3jOYaL46tNVstVh0y/msTDI9/AWtZ4w+QNy8hwTn3GKxfCPhTUNd0DUtRXUWS/Ouvf2N/JBtWYgBSxTrsY7gPagDqP+E31LQLy9s/FdpbI0WnyahBNZMxSVE++mCMhhkfnUKeMvE2nQ6XquuaVYxaRqM0cR8iZjLbeZ9wvkYI6Zx61KPA+pa7eXl74svbWWWawksYYbCNljhR/vNluS2RmoI/BniW+i0vStb1ixm0fTpY5ALeFlmuPLxsD5OAOOcegoAq6l4q8S65oWv32j6ZZNotutxbqZ5mE0wRSHdccAdQM+ldX8P/APknXhzII/4lsHX/AHBXOzeCvE1la6rpOh6zYw6NqEkkg+0Qs01v5n31Qg4IyScn1rsPDelSaH4Y0vSZJFkeztY4GdeAxVQCR+VAGtRWEtt4j/szVI21K1+2ySSGxl8n5YUP3Aw/ix3qSa315jpHk39sgiIOo5iz5w28hP7vOaANmigUUAFFFFABSGlpDQwOV1v/AJHjRv8AsHXv/oy2qt4p48Ia0f8Apwn/APRbf41Z1v8A5HjRv+wde/8Aoy2p2qWX9p6Re2Ak8v7TA8O/Gdu5SM446ZzXzWZO2LT9DspfAeZ+BdbHhPwZe2t6+5Lawi1K13H76yxg7R/203D6msXRYptG0bXv7Qt0vLl9RspZo5s43yMCe45Bb8xXd3fw6gvo/Day3pxpUEUE6iPAu0j2kBvm4GVBqXUvAx1B9Vcaj5f9oXVvcg+T/q/LI4685x+FN4ijzN333+8XK7GLY+OtZvdVMQudLhnF20LaXcBoZwu7G4M2AxxhsDPBrNstc8Q+HbDxXqTvbXRg1XyjEEYbpCUBC+i4PA7V0d54C1LUSlrfa5FcadHcCVWktAbhQG3BfN3cDPfHSnXfgK5nbV4o9YVLPUbtL3ymtgWjkDKT827kHbjpU+1w62tb/ghaRBqGveJ9M8mLVb7QtM8xGkaeVywHIxGqZ3MR3NZuh+IJfEvivwlqM6xiUw38bNECFYpgZAPY9fxrptX8KX9z4mGtaZqcFtMbf7Oy3FqJQoByCmWGDVLw94AuNF1Wwu59XF2to90yKbfazibaTkhuuQT074qYzocnn/w47Sudu/3G+lea+Hf+Ra0v/r0i/wDQRXpTcxk5zx1rzXw7/wAi1pf/AF6Rf+giscLfkl6/5nqZf8bNOkPSlpD0re7PWMTxfOtt4buJWijmAZMo/T74rCPivUW1W4gWWwhaG48pbC43RySJnAZXYgEkc4rp9d0s6zpMtiJvKLlTu27ujA4x79KxNQ8JXuoxzWkurRyWMku/95bh5YxnJVXzx+VdNJwUbSOKqqnOnDbQ63vmimooVFUZwBgZ606udvXQ7I3tqFLY/wDI4eGv+v2T/wBJpqSlsf8AkcPDX/X7J/6TTVvhv4qObHf7vI9UpRSUor3z5MKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApDS0hoAgu/+POf/AK5t/KuV8M/8irpH/XlD/wCgCuru/wDjzn/65t/KuU8Nf8irpH/XlD/6AK8POnaEWdGH3PPfiOLmDx7pWp2u4y6bYtd7B/GqyfMP++c1T+LV+dcsVhs5d1rY28d/K6cgl3VUH5Fj+Vej3nh1L3xPDq8s6mJbJ7Rrcp94Mck5zxx7Gubg+GKQ+ENU0QaozyX8sZF1JDny4kK7Exu5wFx1HWuSjiKUVByesTRqV2Z9/wCLLnRZNTaytba1STVzBcX7RPKkQ2Kd7qvJJ6VV1/WNe1bwxDLFf6VIkWpRRi5tX3LMCy7TgN8uDncp5IrqpfB2oR3F1eafrjWtxNetdYaDdGVKBSjqSNw4654zVFPhzMdP1LfqkSahfXMFyZYLQLDE0RygEeee+efSnGpQsn1FaRHb+JfFl1q2sWsNvYSRaQQJ3JKecfLDbVzwvOSSegqDS/G9/N4m03SbvUNJv/7Qjl8yOwO77Kyxl8FgSG6YrZXwTLLpfiG0vNU8yXWSrSSxQ7PLYIFyBk55H5Gqdv4H1kahot3PrlqRpchCQQ2HloUZdjZ+b7xUkA9BSjLDu+39L/MfvGl8NznwHpp7kP8A+htV+4/5HbT/APsHXP8A6MgqXw1ox8P+H7XTDP5/kA/vNu3dkk9Mn1qK4/5HbT/+wdc/+jIK47p1ZOPn+Rp0NuiiiuZbIoK8x1/xTcaD4o19rOxgjdWs0mvWjd9iMh+ZwOoGMDGK9OrkNQ8HXs+u6jq+n609nc3TQMi+TuQeWpUhxn51IPtiunDShGT59v8AgkTvbQseENbvNXjuvtN5p19HG6iK6sX4kBHIZMkoR710w6evvXM+GvC0ujanf6peXUE13eoistvbiGMKv+yCck+prp6iu4e0fJsON+oUh6ilpD1FZLcZ5T4Y+9rf/YYu/wD0ZW9WD4Y+9rf/AGGLv/0Ya3q9ir8bPdwn8GPoFH+fSikPQj8wO4qEdDOG1HxdqGnzNLLc6aNtwIxYKxklKlsbiynA65wa7r8Me3pXEv4HvjpUulx6zGtqZTIpFr87Nu3As2eea7OIMIkDsGcABiBjJ+nat6zhZcpzUPaXfOPo70UdzXOdL2KOr/8AILn/AA/9CFexr90fSvHNX/5Bc/4f+hCvY1+6PpXr5d8DPn83/iL0Fooor0DyQooooAKKKKACiiigAooooAK5TUf+R6X/ALBv/tSurrlNQ/5Hpf8AsG/+1K4sw/3aRpS+NFfxP/yKmrZ/59Jcn22mvPPA+ut4W8G6lYXrFmsbVL60z/y0jlXIA9cPkfjXpuqWX9paVd2QkEf2iF4t+M7cjGccVyt58O7e+bw+0l4VOlxrDMFjwLpFKsqnnjDDP4189h6kFBwqbXOqSd9DhNAt5dCj1mG/hS7uG1TTGlExPEsg3Mw5GSGY4zkV0v8AwnOsy65dWyTaZbzw3hgTS7sNFLMm7AZXYgEkHIA61t33gY3uoapdLqCoL6+tLwL5X3PJAG373O7HXjFVNX8B6jq6XFjca5FJpc0xl2zWYe4iUtu2LJngcdcZrpdahP3p/wBaIlRkjn11rxHoMvizUhJZ3QhvoohFsYbnYxgAZ+6Np/OtzUPEHibTfJi1K/0LTPNjaY3EzbgORtiVN25mHJJxjn2qxfeArm6n1MRaysdlfTQ3Dwvbh2V42Q53BuQQnp1q5rHhO+uvE41zTdThtpzai2dJ7YTbQCTlCWG081DqUJNXt/SQPmOb8OeIZvE/ifw7qU8caym0vI38vO0lWAyM89P516ge9cP4Y8A3Ghapa3U2ri7jtzPsU2+1m80gkk7vb0ruP4a5sVKnKa9nt/wSoppanHfDL/kT/wDt9uv/AEc9djXHfDL/AJE//t9uv/Rz12NRif40vVlR2CsfxXKtv4T1WV4kmVLWRzE+cMAO+OcVsVQ1vTv7Y0S900SCL7VC0e8jdjI64zWVNxUk5bDex55c+N9UtL3+z4ptN0mOC0hktlv42C3RZMkK/AUA8DPevSrGaS5sLeeaNY5ZIld0Vw4UkZIBHB+tche+CdWkhktbXXIjZzwJE8N3ZiYIVUKWjyw25HP1rqNF0uPRNEs9MhkeSO1iWJXfq2B1/wDrV04iVKUE4bkQ5r2ZfooorjNDE8Wf8gL/ALerb/0clbdYniz/AJAX/b1bf+jkrbrR/AvViCiiisxmH4o1O80rShNYmzR2kVHnvJljihU9WOSM/SsfwX4quNb1fVdMnvLK/FksbJd2ilFcNkEYJ7betaXivw3J4hhsTBdx289lP58fmwiWNjjGGXIyPeq+heF9S0rxJeaxe6vFdm7gWKSJbYRKpQ/LtO44GCRj3Bruh7H2LTepm3K51I/Olo/rzRXCjQK4jW/+R0m/68If/Q5K7euI1v8A5HSb/rwh/wDQ5K6cL8T9Dowv8aIzvWFrGpX6ara6VpnkrcTo0ryzAlY0HHQcnk1u965XxVsttSsL5LmaynVWjF2IPNiAP8LgHOT2+lejS31PWrNqOnkU38Vatay3VhcQWz6gLqK1t9hIQlwTvPfHtUl/4n1Tw3cNbaxFb3Ek0Je1kgBQO4IG0g9uetUdG0GbWl1O7lvJxK93HcWt60PllnUfeCE/d6jr0rWm8IS6rLLPrWofaJ/JMMJii8tYSSCWAycnIHtXTL2UXZnHF15K8f69RJda1jRJrSTWRbS2lzlf3ClXiYLuwc9eBWJ4hvtf1LwRcajIlotjcorCBQd8aFhhs+pwPzroV8N3l3cWzazqS3lvbAmOFIAm84xljk54NUrnwZqc+jPoq64Rpq48tTbZfAOQpbPTp+VKE6V0+o5xqtNO9rHYR/6tMdlFOPQ/SkQYQLnOBjOOtL2P0rkW/wDXc7+h2/gf/kQ9B/68If8A0AUUeB/+RD0D/rwh/wDQBRX0S2R8ZLdnQUYooqxBgZzijA9KKKACjAoooAKMD0oooAMUUUUAGKKKKAGSwxTLtliSRfR1BFOCqoAAAA6AClooATA9KWiigAwKKKKACiiigAooooAKKKKACkpaQ0Acrrf/ACPOjds6fedf+ultV38OtUPGX2eSfT4kS8bVD5jWrWcipIqADzMlgV28oCGBBJHpXPfZvEZJzca51/5+bH/4zXgZjh1OtfnS9TppTajsdh+FH4Vx/wBl8R/899d/8CbH/wCMUfZfEf8Az313/wACbH/4xXB9Tj/OjT2j7HYenHSiuP8AsviP/nvrv/gTY/8Axij7L4j/AOe+u/8AgTY//GKPqcf+fiD2nkdhR+Fcf9l8R/8APfXf/Amx/wDjFH2XxH/z8a7/AOBNj/8AGKf1SP8Az8Qe08jr3ztIxnIrzXw7z4Z0vn/l1i/9BFbZtvEGPnm19lHULdWWfwxCD+orIsNGS6sIZ9Ch1+HTXQGBDd22NuOwkRmx1PX6YGBXRRoRhCSc1udOGxSpTu0X6KhHh3VP+o7/AOBdn/8AGaX/AIR3VP8AqO/+Bdn/APGavkp/zo7v7Th2ZLijFRf8I7qn/Ud/8C7P/wCM0f8ACO6p/wBR3/wLs/8A4zRyU/50H9p0+zJcUVF/wjuqf9R3/wAC7P8A+M0f8I7qn/Ud/wDAuz/+M0clP+dB/acOzJaLD/kcPDeOv2yTj2+zT/4iov8AhHdU/wCo7/4F2f8A8ZqbT1i8O6vbXOoWGs3V9IWisBLNDIvmEcgeWqBTtDcnOF3VtQjTjNPnRhiMfGpScEnqep+lKK5j/hI9Y7eHHx/1+R/4Uf8ACR6x/wBC4/8A4GR/4V6X1yh/OjxOSXY6eiuY/wCEj1n/AKFx/wDwNj/wo/4SPWf+hcf/AMDY/wDCj65h/wCdD9nLsdPRXMf8JHrP/QuP/wCBsf8AhR/wkes/9C4//gbH/hR9cw/86D2cux09Fcx/wkes/wDQuP8A+Bsf+FH/AAkescf8U4/JwB9tj5o+uUOkw5JdjpzSZrh3+IoaeS3t9FvHngbZcJI6oImxnGSeeCDx60D4gXYAH/COzfjdR1p9Yp/zGkcLWkrqJ3GTRk1xH/CwLv8A6F2b/wACo6P+FgXf/Quzf+BUdH1il/MP6nX/AJTt8mjJriP+FgXf/Quzf+BUdH/CwLv/AKF2b/wKjo+sUv5g+p1/5Tt8ml7Vw/8AwsC7/wChdm/8Co6T/hYF2f8AmXZv/AqOj6xS/mB4Sv8Aync0orhP+FiG3/e32jXFvaL/AKyYTo4jGQMkA9B1zXdLyo5z71cKkZ6xZlUpTpu01YWkNLSetWQQXf8Ax5z/APXNv5VyvhrP/CK6RjH/AB5Q/wDoArrpWVUcuRtCktnpivII9Sk8sHRW16HTTzAomttoTttEkbMB6ZPT2ry8yoe2jHVL1OjDRnJtRVz0Xn2owfavPP7T1j/nvr//AIEWP/xij+09Y/5+Nf8A/Aix/wDjFeR/Z7/nR1+yrfyM9D59qME+leef2nrH/Pxr/wD4EWP/AMYo/tPWP+fjX/8AwIsf/jFH9nv+dB7Kr/Iz0Pn2o5Pp6V55/aesf8/Gv/8AgRY//GKP7T1j/n41/wD8CLH/AOMUf2e/50Hsqv8AIz0Pn2rGuDjxtp2f+gfdf+jIK5X+09Y/5+Nf/wDAix/+MUxLlrjU7cTjxG+rMrLaSi6tgAvBcfKgTooPzKegq4YDlbfOtn+RM4VIq7iej0ma5A2viPP/AB8a4fcXNl/8YpPsviP/AJ+Nd/8AAmx/+MVj9Tj/AM/EZqo+x2FHp7Vx/wBl8R/899d/8CbH/wCMUfZfEf8Az313/wACbH/4xR9Tj/z8Qe0fY7AcY9qOgxiuP+y+I/8Anvrv/gTY/wDxij7L4j/5767/AOBNj/8AGKPqcf8An4g9p5HYUf16e1cf9l8R/wDPfXf/AAJsf/jFIbbxAVYPLrzLj5gt1ZAkemRCCM/h7U1g43+NA5u2xzPhggnWyOc6vdEf991vDkVWjsNL1LMnhi21eKOPCXLQzxIDKOoYSq+ZP7xGMnrUn/CO6p/1HP8AwMs//jNehVpw53eaPRoZhCFNRcXoTUVF/wAI7qn/AFHf/Auz/wDjNH/CO6p/1Hf/AALs/wD4zWXJT/nRt/adPsyTFLUX/CO6p/1Hf/Auz/8AjNH/AAjuqf8AUd/8C7P/AOM0clP+dB/adPsyWiov+Ed1T/qO/wDgXZ//ABmj/hHdU/6jn/gXZ/8AxmnyU/50H9p0+zK2sf8AIKm/D88ivYl+6K8kfTU0spe65b65PYwsGdGnt3QEHgsscasRnHfHrXaJ4m1aRA8fhyXYwyu+7jU49xzg+1d+FqUqMPemtTysdW+sTTinodRRXMf8JHrP/QuP/wCBsf8AhR/wkes/9C4//gbH/hXR9cw/86OP2cux09Fcx/wkes/9C4//AIGx/wCFH/CR6z/0Lj/+Bsf+FH1zD/zoPZy7HT0VzH/CR6z/ANC4/wD4Gx/4Uf8ACR6z/wBC4/8A4Gx/4UfXMP8AzoPZy7HT0tcx/wAJJrH/AELb/wDgYn+FXNJ15tRvJrK5spbK7jQSeW7BgyEkZUjrg9frVwxNKbtGVxOLW6NuikFLW5IVymonHjpfX+zf/aldXXHeL1ik1OzS1W+/tXy2KvZyJGRFkZ3GRShGTwMZz0rlxseahJN2Lpu0kalJ+Fcf9m8Rn/lvrv8A4E2P/wAYo+y+I/8An413/wACbH/4xXzf1OP/AD8R1877HY5NJ0x7Vx/2XxH/AM99d/8AAmx/+MUfZfEf/PfXf/Amx/8AjFH1OP8Az8QvaPsdh+FHXqK4/wCy+I/+e+u/+BNj/wDGKPsviP8A5767/wCBNj/8Yo+qR/5+IPaeR2H4Uds9P8K4/wCy+I/+e+u/+BNj/wDGKa9tr3lsJ5dfeHHzqlzZ5I7gbYgenoc01hI3+NA6jtsO+GR/4o4YGf8ATbn/ANHNXYc152mo6cGc+FRrMFpwJRbSwLH5gHzfLMjndngkYBIOad/aerf8/PiD/v8A2P8A8Zror4HmqN86NIQqtXUD0Ln2o59q89/tPVf+fnxB/wB/7H/4xR/aeq/8/PiD/v8A2P8A8YrH+z3/ADor2VX+RnoWCBjijn2rz3+09V/5+fEH/f8Asf8A4xR/aeq/8/PiD/v/AGP/AMYo/s9/zoPZVf5Gehc+1LzXnn9p6r/z8+IP+/8AY/8Axij+09W/5+fEH/f+x/8AjFH9nv8AnQeyq/yM6jxX/wAgLn/n6tuf+2yVtV5xcX9vJGB4iXxBc2O4AIZ4MeYThD+5RGzuxjJxnB7VrC118KBHNr6oOArXVkSB6HMJOfxNVLAqMEnNdTKTlB2lHU7GiuP+y+Iv+e+u/wDgTY//ABij7L4j/wCe+u/+BNj/APGKy+px/wCfiF7TyOwzR+Fcf9l8R/8APfXf/Amx/wDjFH2XxH/z313/AMCbH/4xR9Tj/Og9p5HYfhRXH/ZfEf8Az313/wACbH/4xR9l8R/899d/8CbH/wCMUfU4/wDPxB7R9jsK4nW+fGs4/wCofD/6Mkqb7L4j/wCe+u/+BNj/APGKzpNPt7rUTCbbxAdfVA805uYQfJJwuTs8sjOQAFzkH3rehhYxbfOjSliFTmpNFgdKXFQjw9quBn+3c9/9Ls//AIzS/wDCO6p/1Hf/AALs/wD4zWqhD+dHpvM6fZkmBjGKXFRf8I7qn/Ud/wDAuz/+M0f8I7qn/Ud/8C7P/wCM0+SH86F/adP+Vkv9aKi/4R3VP+o7/wCBdn/8Zo/4R3VP+o7/AOBdn/8AGaXJD+dB/adP+VktIeh7cE5qP/hHdU/6jv8A4F2f/wAZpr6BeojNcx6/JAozIqXdrkr3+7ED+RzTUIfzoHmUGrcrO98D/wDIiaD/ANeEP/oAorR0drN9EsW09QtkYE8hQMYTaNo/KivePnnq2y/RRXDeOdd8XeGrO+1bT4tEl0u2RW23Bl849Afu/L1qyTuaK89u/FHirQbSwvPECaGlrd3UMJa2aX92r53Ft3AwO/Sur0vxLoutrO2m6pa3Qg5l8uQHYPU+nTrQBr0Vi6Z4q0PWL6Sz07VrS6uEBJjilDHA6kDvV6+1Gz0qze81C6itraMfNLK21R+JoAuUVkaf4l0bVbWe6sdUtZ4IOZXWQYjHXn0GKp2/jjw1eRXL2mt2Uwt4zJJslBKqP4senT86AOjornPB3i208YaCmp2xjRiSssKy7/LYHgE4H1qxe+LvD2m6h9gvdYs4LvIBieUAj6+n40AbdFYuq+KdC0N449T1a1tXddyrJIMlfXHp79KyNS+IWj6f4l0fSftdrJFqMbyG5FwMRgDKfUN0HI6UAdjRUE9zDaWz3FxMsUMa7nkkbAUep9KzdL8VaFrTSrp2q2ty0Q3SLHJyo7k+1AGzRXP2/jXwzeXsNpba7Yy3E2PLjSdSWJ6Ae/tTvF3ie28J+HrnVbkofL4jjkfZ5r9lBweTg9u1AG9RXD3HxE0+DWtIR7uzj0m+sZbhrppPuurKu0HoeSR07V0MXiTRZtHOrx6raHT14a480bAfQnPB9qANeiszSde0vXYnm0vUILuNDtYxODtPbI6jNR6n4l0XRZvJ1LVbW0l8vzQksoVimcZwff0oA16K54eOPC7SWyLr1gzXODDidfnycDmptT8WaBotyltqer2ltORnZJIAcepHagDborkZvH+lQ+NLPw99ptmF1bGYT+eMByyhEx3LA5HP4V1o6CgBaSlpDQwOV1sZ8caN/wBg69/9GW1Xqo63/wAjxo3/AGDr3/0ZbVer5fNv94+SO2j8IUUHODjrjj6159rGpeM9M1/SdNF/pbDVJpY42+zN+6Cru5554IrgpUnUdk7Gjdj0GiuDi8ZT6F4kn0nxRf2u2OzSZZIIGBdmcjgcngAetdDN4t0K30i31STUofsdx/qnXLFyOCAoGTjv6VUsPUi1pcXOjboqlperWGtWYu9OuUngLFdy5yD6EHkGrnasWnFtMpAeh+lY3g7/AJErQv8AsHwf+gCtk9D9KxvB3/IlaF/2D4P/AEAVqv4cvVfqT1NrFFFGM9OtZFBSVgSeN/DkWpnT31aEXHmCM/K2wN6FsYzz0zReeNvDunai1jdanHHOjhHG1iEY9mYDap6dTWnsanRC5kdBRXKWPjeyvPGt34fEifu40MThWy7HO4cjGAB175rqx0HapnCVN2kgTvqFYeu/8hnw1/2EX/8ASaetysPXf+Qz4a/7CL/+k09XR+L5P8mDNyiiisRhRVLVNWsdFszd6hcJBCCF3NkksegAGcms6Hxn4dn06a/j1SL7NAypIzghlYngFSN2SOnHY1caU5K6QuZG9RXH6l8RNEh8N3+qafeR3D2wx5TI6ncwO0EEZGcdfauh0bVrfW9Jgv7Vw8cq54UjB6Ec+hyKqVGcFeSEpJl+j+tFHp9az6lHnzf8jJ4h/wCv1B/5Lw1Yqu3/ACMviH/r9T/0nhqxXqvaPovyPcwv8FBRRVPUtUstJtxNe3CwoTgEjJJ9gOtJJvY3bS1ZcorITxPoslkt2NRhEBfy9xyMHrg+nH51PpeuabrJdbG5ErJgsu0qwB6HBq/ZyS2IVSD2ZoUUgpahGljL8Sf8ivq3/XlN/wCgGvaV+6K8W8Sf8ivq3/XlN/6Aa9pX7or1su+Bng5v8cfQWkpaSvRPHK97xY3J7iNsflXkehgDQNOwP+XaP/0EV65e/wDHhc/9cm/lXkeif8gDTv8Ar2j/APQRXm5h8MT2Mn+ORfooqG5uIrS3luJ32xRqWZsZwO/Hft+deWld2PdbS1ZLS1w9t49huBpE0kkMMM8kyXWQTsCrlce546etdNa69pV7YzXsF5GbeE4lZsrsPvnmtJUZx6GUK9OXU0qKzdN17TNXd0sboSugyU2lWx6gHtWl2rNpp2ZqpKWwUy1/5GnRf+usv/op6fTLX/kaNF/66y/+inqZfC/RmGL/AIMjvKKO5orzDwwpKp6pq1hotmbzUblLeAHG5snJ9ABkk1nQ+M/Dtxps+oJqkX2aAhZC2VZCemVIzz24q40qkldIXMjeoritZ+JWiWfhy81LTrtLmeDaqwujoSxPcEcAjPPtXVabqNvqunQ3trJvilXcrBSM/nTnSnCPNJCUk2W6OvXtRRUR3Gzjvh5zYa5n/oN3n/oddjXHfDv/AJB+uf8AYavP/Q67GtsT/GkKGwUUVg6l4z8P6RftZ32pRxTrjeuGIQkcbiAQOOecVlGMpaRVxt2N6isDUfGvh3SrkW93qaLJtDkIjuACMgkqDjPv9aoP4808eNbfQUliaKW28zzhuJ3kgBRjgjBzmrjQqPoLmR12KMUfWisvUoxPFv8AyKuof9c/6itoVjeLf+RV1D/rn/UVtDpVv4F6sQUUUf5xUDCisC68a+HLPUTp9xqkSXCtsbhiqn0LY2jt1NM1Dxz4b024lgutUjSaJtroFZiuQCDwOhBHPStPY1P5SeZHRUVHBPFcwRzwSLJDKodHU5DKeQRUlZtO+pQYqjYj/iuB/wBg1v8A0YtXqo2X/I8D/sGt/wCjFr0Mr/3lfMyrfCdVS0lLX1RxBXKah/yPa/8AYM/9q11dcpqH/I9L/wBg3/2pXHmP+7SNKXxIvUUUV8idwUVyviLxrZ+Htf0vTLh1UXRYysVYmNcHaRjrkjFUdE+I2lSwyR6vfQwXgu5YQoQ7VAcqgZugPHet/q1Rx5kieZXO4orD1PxhoGj332O91KKKfglQCwUHpuIBC/j/ACraR1kjV0YMrDIYHIIrKUJR3Q00x1B6H6YooPSpW4M8l8G/8gKT/r7uf/RjV0Nc94N/5AUn/X5c/wDoxq6HoK9iq/fZ72G/hR9EHSiobq6gs7WS5uHEcMalnc9h61kDxhoB3galH8q7uVYbh/s5HzfhmpUZSV0jSVSMXqzdorJXxJo76a1+t/H9nV/LLEHO702/ez+FZmq+ONMtNGlvbKZJ5EdUEbKynkjqCOOOfeqVKbdkiZVoRV2zqaQ9Kit7mG8t0uIH3xPyrYxmpjUdbM0TvZoo6qB9nt+P+Xy2/wDRyV6OOgrzjVf+PeD/AK/Lb/0clejjpXNifhj8zycd/EXoFFFQXd3b2NrLdXcqxW8S75JGOAorjSb0RxXsT0Vhab4x0DVWnFpqURaCMySB1KbUHVvmxwPXmq0fj/w1cCUW+pozpE0oBjYbgOSVyPm/CtfYVL6IlyR01Fc/4N8UQ+LNCS/jCrIDtljUNhD2GT14xXQVnKLhJxa1GncKxIP+R3vv+wfb/wDoc1bdYkH/ACO1/wD9g+3/APRktXT6+gM28DPSig9aKyGGKKrX+oWml2Ul5fXEdvbx/fkc4A/+v7YrKs/Gfh+/tLq5t9SjaO0TzJwVZWRfUggHHvVxp1JK6RPMb1FYWn+MfD+q6gthZamk1wwyoCsA4HocYP4Gt3qM+tEoyjo0NO4Uh6fQGlpD90/Q1K3GJ4K48D6EP+nGH/0AUUvgr/kR9C/68Yf/AEAUV91H4UebLdm9XH/FCN5fhzrKRIzuYRhVXJPzCuwpCoYEMAQexpgef+ObOO+0TwvbSw+bC2pWu9Cu4Fe+R/jWH460i9n13XrfRbcxTz+H8KIVA3kSglRjHJXIr1wqDjIBxzRtGc4GfWgDxDw9DHqHiTwsbXxDJfTWblhBbaKlv5CBMMsrhhgYwMc884rtPiOgUeHr65t3n0q01FZb1FjL4TaQGZe6g47V3SxxqSVRQW6kDrSlQRggEUAeTeINc0W90rxLfaD4eXUFWzijnutjLDOd/wB0qMFto+Yn8KyNMvLe4+IuiM+qw6nBJZT2+6DTfIgRivEY67uncmvbxGipsCKF9AOKQRRjGI1GDkYHQ0AcP8KJrb/hCbe0jTy7i1d47hDGVKtuPXjnjFcP4qax0/WfE0tpem2vJ5d0+karp4nivyF4MTjlVPQHPWvcgirnaoGfQU0xRsQSikjoSOlAHi+o6w0uuzpeCDw/KdMt/lOnG6uLndGCY03HHyklcYJyKpeGJrO0t/hvd30JEUEd7bXDvAW2SdFVuM5yeK92MaMwYopYdCRyKPLTAGxcA5xjvQBxvxOtLi68HkRQSXEUN1DNcwxjLSQq4LgDvx2rmru90rxD4v0abwvCGWytZ/tk8MJjRYjGQsbHA5zjjtivWcD0pqxomdiKuTk4GM0AeLx6TFbfBbwq8NiI7sXtpKWEWJATLy3r0Jrv/iPb/afh5raLD5ji1YooXcc47e/Wup2JgDauB0GKXA9KAPMrWLT9Y8deEruG2SWzj0mdoyYcKrhlBGCODkH9a5TXNNuo31d7fdaada+JvOmZLUTLGpiGH8s8MAxzXu4RVxhQMdMCl2Lz8o568daAPMPAMAn8banqMOtSatG1oscs8WmrawFt2VAIb5nGGzxxnrWjeafDefGy0lubVZo4tAZkZ03Kj+fj6ZwTXerGiLtRFUegGKXaN27AzjGcUAeISaNBD8GvFJj09VuP7TnZCIvnwJ1C44z0HapPFOoCbXvFdrLcQaXI0axLFHp32i41AGMYIZsgDJwMD37V7VsXaV2jB6jFHlpvD7F3DoccigDxnwxcWMPiHwTe3MYEUuiPbCVoicTiRcKTj73BHNesazq1poWlzajfO628Iy5RCx/ACr3lxgAbFwvQY6UpVSMFQRjGCKAMyfXLK21Sw0+R5PtF8rNABExBCjJyeg696dY6zaahqF/ZW7SGaxcJMGjYAEgEYJ4PBFaO0ZzgZHfFJtUEnAyfahgctrX/ACPGjf8AYOvf/RltV6qOt/8AI8aN/wBg69/9GW1Xq+Xzb/ePkjto/CFcd4rgmm8b+DXSJ2jjnuN7AZC5jGCT2rsaK8+nU5Hc0aucfbWW74s6heSWxKDS4lSVkyAS7ZAPrXn0GmX1lJYahNLfWFlBdXsZmt7cSNCzSZU7SDgEA8ivcetFdNPGOPTt+BDgjivh/aeS2r3qSX80V1ch1nu4hH52BguqAAgH3Fdp0GPSjFL2rmq1PaSci0rCHofpWN4O/wCRK0L/ALB8H/oArZPQ/Ssbwd/yJWhf9g+D/wBAFUv4UvVfqLqbVNkBaNlVsEjAPv2p1FZxbTuM8p0fULfSvCB8NX3h24vdXE7LJaPCQlwxl3CQyYIxjHJ6YrE12e8vdI8Q2ki31rdPcOyaXYWA8t1DZ8x5NpLAjnqK9x70V3RxijK/KQ4Hn2gXSQeOpJZILhY7/TLYQSGFtu5Q27LY+U8969B60YGAOw6UVy16vtZXKjGyCsPXf+Qz4a/7CL/+k09blYeu/wDIZ8Nf9hF//Saeij8Xyf5MbNyiiisWM5L4gQW1xolt9rgvnjju45BPZH95bMOku3B3AemK8/n1HVhPMUVr/TTe2+7WZ9K/fQ43clMfOy8DdjjPuK9tpMDGMDHpXbRxfs48rVyJQu7nigSa9vfFwibVL573SV8m4urfYZihO7ACgADpgjNeseHb2C/0CzmgSRUEartkjKMCODwee1alFTXxPtVa1gjBphR6fWij0+tcvUs8+b/kZfEP/X6n/pPDViq7f8jL4h/6/U/9J4asV6r2j6L8j3ML/BQVy/iIG08Q6Pqs8MktlAJUcopYxMRw+AP1rqKTPNVCXKzWpDmVjza6tm1bxOuqW9jKunTXlsnzRbRKVzucg9B0FdVFC6+PZ5BGRG1gql8cE7jx9a3++O1L1rSdfmMIYdR6+Ynfjp2paKKw6nUZfiT/AJFfVv8Arym/9ANe0r90V4t4k/5FfVv+vKb/ANANe0r90V62XfAzwc3+OPoLSUtJXonjle9/48Ln/rk38q8j0T/kAad/17R/+givXL3/AI8Ln/rk38q8j0T/AJAGnf8AXtH/AOgivNzD4YnsZP8AHIv0jDKkAZ470tJzXlJ21Peaueb6NB59x4btJLWXdaXV152+E7QSpKnOMY6VNr2mXl3feJ/s8Uu0tZS4RMl1AO/GRgnpx7V6GeetJgHqM10/WWnf+t7nH9UXLa/9WscPocYvfE1tdxXmo3hggZXlngEapn+E/KMn8a7mg8n3orKpPndzejS9mmgplr/yNGi/9dZf/RT0+mWv/I0aL/11l/8ART1lL4X6MjF/wZHedzRR3NFeWzwzi/HMUkOqeHdWktZLrT7G5drmOOMuVLKAj7RnIBBrnfEOtrqM0t/pmhGG1NzCkusS2hkk4yd6xFcnacDJz1r1ajHGO1dlPFqKV46ohxueG36T383izyTqt/JdaXG0U9zbbGlKSZcKoUcAHjjsa9k0a8gv9Itri3DrG0YwHQoeOOh6VewMYxxR/Wpr4n2sUrW/4YIxswooornjuUzjvh3/AMg/XP8AsNXn/oddjXHfDv8A5B+uf9hq8/8AQ67GtsT/ABpCjsHTHtz9PevJfFPl2Wua69rLqWnXtwq5tzafarbUAE44xx/d6+tetUYHp70UK3spc1rhKNzxGS81K40/VrbUbe80m6mgVU07TtP5uT5QGWkKnjtjPAFbvh+cW/iPwrdy29wIZNG+yiQQMdsoccEYOOnU44r1DAznAzS//rroljeZW5bEqmA6UUUVwdTQxfFv/Iq6h/1z/qK2u1Yvi3/kVdQ/65/1FbXarfwL1YgpCDggHB7H0paKhPlfN2A8khu4dK8Hat4ZvdKupNYllmCotuzC4Z2JR9+Mdx9MVoeDNFuLG38WRXtsTcmOKEyNGSZMW4Bwe/P616WeTk9aO+a65Yv3WktyOQ53wHFJD4E0WOaNo5FtUBRxgr7GuioormqS55uXctKwVRsv+R4H/YNb/wBGLV6qNl/yPA/7Brf+jFruyv8A3mPzM63wHVUtJS19UcQVymof8j0v/YN/9qV1dcpqH/I9L/2Df/alceY/7tI0pfEi9QKKK+RO44zxlJ9k8Q+GtQkglktre4kErRRNIV3RlV4A6ZNc+mmOvwm8QxiyYXMt1PJs8vDE+bxx1OMcV6n2xRXVHFOMVG2xHLqeKvZz6Zda5b6jf6vbG9PmRxW1mJVukZAMbipweMY9q9T8L2b6f4W0y0kEwaK3VdsxG9eOhxxkdK1yATkjmilXxLqxUbBGHKFB6UUHpXMijyXwb/yApP8Ar8uf/RjV0Paue8G/8gKT/r8uf/RjV0NexV+NnvYb+FH0Rj+K42l8K6nGiM7tAwUAZJ6VkXNkP+Eg8IFbb93FFNvwnCYjXGfTnNdfSHJz3zTjV5Vb1/Ic6Km7nnN8bmz1DUmWAxwvqu43H2bzTEPLB3KvrnjNZ92ks6+IDGL+6MqW8qSXEO1pQrjcQAo4H9K9W69aWtVibdDCWDv1ILaeO7s45oQwjcZUMpUgfQ1MelFB6Vzbu52RVtClqv8Ax7wf9flt/wCjkr0cdK841X/j3g/6/Lb/ANHJXo46Vy4n4Y/M8nHfxF6BWH4ugiuvC1/DPZT3kLR/PDA212GRyp9utblFc0JOMk0cLVzxeS91FPtP9lLP4ht106RcalYbJIBxhd+F35/u+1LbXDTeK/Ct0bzU76KITQu8mn+TBAzwsqKq7R/FgdT0r2fjn360DAPH1ru+vK2kejM/ZnIfDeUDwjBZtDNDcWjNHMksRTByTwT16119FFcVWfPJyNErIKxIP+R2v/8AsH2//oyWtusSD/kdr/8A7B9v/wCjJadPZ+gM2z1ooPWist9BnHfEO0nn07S7pLZ7u2sdRiubq3jXczxDOcDv24rk/FMh8U6healotlci3tdHuYLiZoTGZmYDbGAQC2DzxXrtAAAwOBXZRxfs0rLYhwucNqVh5L+BhBalVgulLbEx5amFsk+nOK7kdOaKKwqVHNJPoNJIKQ/dP0NLSH7p+hrNblB4K/5EfQv+vGH/ANAFFHgr/kR9C/68Yf8A0AUV91H4UeZLdm9RRRTGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlAHM+I7O+Gsadq1lZveC3hnglgjdVcrIY2DLuIHBjAxkfe9qq/2lqn/Qrav/33b/8Ax2uv70vTgVw18BRry55bmkKsoqyOP/tLVP8AoVtX/wC+7f8A+O0f2lqn/Qrav/33b/8Ax2uwzRmsf7Iw/n95XtpnH/2lqn/Qrav/AN92/wD8do/tLVP+hW1f/vu3/wDjtdhmjNH9kYfz+8PbTOP/ALS1T/oVtX/77t//AI7R/aWqf9Ctq/8A33b/APx2uwzRmj+yMP5/eHtpnHHUdU6Dwvqucd3t8f8Ao36dqo6ENc0bQrDTbrw3eyy2tukJktpYGRtoAyN0gPb06/nXfUdsY4q/7LocvLr94vbTOQ/tLVP+hX1f/vq3/wDjtH9pap/0K2r/APfdv/8AHa7DNGaj+yMP5/eP20zj/wC0dT/6FbVv++7f/wCO0f2jqf8A0K2rf992/wD8drsM0Zo/sjD+f3h7aZx/9o6n/wBCtq3/AH3b/wDx2j+0tU/6FbV/++7f/wCO12GaM0f2Rh/P7w9tM4/+0tU/6FbV/wDvu3/+O1najFr+o3um3Vv4fuIl0+c3DJczxK0uUZNqBWIzhyeSOleg5pOPSqjldCLur/eJ1ps5D+0tU/6FfV/++7f/AOO0f2jqn/Qrav8A992//wAdrsM0ZqXlOHeuv3j9tM4/+0dU/wChW1f/AL7t/wD47R/aWqf9Ctq//fdv/wDHa7DNGaP7Iw/n94e2mcf/AGlqn/Qrav8A992//wAdo/tLVP8AoVtX/wC+7f8A+O12GaM0f2Rh/P7w9tM4/wDtLVP+hW1f/vu3/wDjtH9pan0/4RbVz/wO2/8Ajtdhmij+yMP5/eHtpnlEmj+JItT1C9fQZZUvpRKEgniLxYRUw+5lGSEB4J6077Hr/wD0LGpf9/Lf/wCO16rikNdH1GkdEMxrU42R5X9j1/8A6FnUv+/lv/8AHKPsev8A/Qsal/38t/8A45XqlFH1CkX/AGrX8vuPK/sev/8AQs6l/wB/Lf8A+OUfY9f/AOhZ1L/v5b//AByvVKKPqFIX9q1/L7jyv7Hr/wD0LOpf9/Lf/wCOUfY9f/6FnUv+/lv/APHK9UoxR9QpD/tWv5fceSXmg+INYsp9OGhXVqt0hhae4li2RKRgt8rknAPTBzivW0+6KMA0o6VvSoxpLlictfEzrtSmLSUtFamBDcR+bbyx7tu9CufTIxXlFpo/iHTbOGyk8PXk5t0EXmwSwlH2jGRlweceleuYpOlY1aEaukjehiZ0HeB5V9k1/wD6FjU/++4P/jlH2TX/APoWNT/77g/+OV6rRWP1CkdX9q1/L7jyr7Jr/wD0LGp/99wf/HKPsmvf9Cxqf/fcH/xyvVaKPqFIP7Vr+X3HlX2TXv8AoWNT/wC+4P8A45R9k1//AKFjU/8AvuD/AOOV6rRR9QpB/atfy+48q+ya/wD9Cxqf/fcH/wAcottO8RDWbG/bw5drBaMzOjTQiRyylRtAfBAyc5Ir1WlwD1o+oUSJ5lWqLlZx41LVMf8AIr6v/wB92/8A8do/tHVP+hW1f/vu3/8Ajtdh0ozXP/ZGH8/vOf20zj/7R1T/AKFbV/8Avu3/APjtH9pap/0K2r/992//AMdrsM0Zo/snD+f3h7aZx/8AaWqf9Ctq/wD33b//AB2j+0tU/wChW1f/AL7t/wD47XYZozR/ZGH8/vD20zj/AO0tU/6FbV/++7f/AOO0f2jqp4Xwvqm7/alt1H4nzTj8q7DNJ17U1lOHTvr94e2meceH9L1zwzb3cNxo9xe/bLmS832kkX7tpDlkYO68g8ZANbH9pan/ANCtq3/fdv8A/Ha7DGKM1VTLKE3zO/3iVWaOP/tLVP8AoVtX/wC+7f8A+O0f2lqn/Qrav/33b/8Ax2uwzRmo/sjD+f3j9tM4/wDtLVP+hW1f/vu3/wDjtH9o6n/0K2rf992//wAdrsM0Zo/sjD+f3h7aZx/9o6p/0K2r/wDfdv8A/HaP7R1T/oVtX/77t/8A47XYZozR/ZGH8/vD20zgdZXXNZ0m40+28PXcEk6hfNu5YVRRn/Zdjnj0q6uo6uEAk8L6mXx8xjktypPsTKOPwrsKM1X9lUOW1n94vbTOQ/tLVP8AoVtX/wC+7f8A+O0f2jqn/Qrav/33b/8Ax2uwzRmp/sjD+f3j9tM4/wDtHU/+hW1b/vu3/wDjtH9o6n/0K2rf992//wAdrsM0Zo/sjD+f3h7aZx/9o6p/0K2r/wDfdv8A/HaP7S1T/oVtX/77t/8A47XYZozR/ZGH8/vD20zj/wC0dU/6FfVv++7f/wCO1Y0W21C41+XVLuwksYltvs8cUzo0jHduJwhIA4A611GaMA1tQy6jRnzx3JlUlJWYgOQKdSYpa7UZhXLa7a6hB4gh1S0sJL6L7MbeWKF0V1+YMGG8gH8xXU0h61FWnGpBwlsxptO6OP8A7R1P/oV9X/76t/8A47R/aWp/9Ctq3/fdv/8AHa7CjpXn/wBkYfz+819tM4/+0tU/6FbV/wDvu3/+O0f2lqn/AEK2r/8Afdv/APHa7DNGaP7Iw/n94e2mcf8A2jqn/Qrav/33b/8Ax2j+0tU/6FbV/wDvu3/+O12GaM0f2Rh/P7w9tM4/+0tU/wChW1f/AL7t/wD47TW1HVipEfhfVN5Hy75LcDPufNzj6V2WaTAPWmspw6d9fvB1p2PH9P8AC3iHw7btZnSJb5GkeYS2kseBvJYqd7KcgkjvnGaufY9f/wChZ1I/9tLf/wCOV6ptFFdEsFSk7s6IZlWhFRXQ8r+x6/8A9CzqX/fy3/8AjlH2PX/+hZ1L/v5b/wDxyvVKKX1CkV/atfy+48r+x6//ANCzqX/fy3/+OUfY9f8A+hZ1L/v5b/8AxyvVKKPqFIP7Vr+X3Hlf2PX/APoWdS/7+W//AMco+xa9/wBCzqX/AH8t/wD45XqlFH1CkP8AtWv5fceS3Gh+JdRjSGDQZ7d1lSXzLqaJV+Rg2Pldjk7cDjvmurGo6rgbvC2qg9wsluQPofNrr8Z60vSonl1CasznqYupVlzSOP8A7S1T/oVtX/77t/8A47R/aWqf9Ctq/wD33b//AB2uwzRmsv7Iw/n95HtpnH/2jqf/AEK2rf8Afdv/APHaP7R1P/oVtW/77t//AI7XYZozR/ZOH8/vD20zj/7R1T/oVtX/AO+7f/47R/aWqf8AQrav/wB92/8A8drsM0Zo/sjD+f3h7aZx/wDaWqf9Ctq//fdv/wDHaz449fj1+fVm8P3H2ea3S38gTRechUs24jdtwd5H3s8dK9AzSVUcroR2v94nWmzkP7S1M/8AMrat/wB92/8A8do/tLVP+hW1f/vu3/8AjtdfmlzU/wBkYfz+8ftpnH/2jqf/AEK2r/8Afdv/APHaP7S1T/oVtX/77t//AI7XYZozR/ZOH8/vD20zj/7S1T/oVtX/AO+7f/47R/aWqf8AQrav/wB92/8A8drsM0Zo/sjD+f3h7aZx/wDaWqf9Ctq//fdv/wDHaa+oauyMIvC+pByMAyyW4UHsSRLnH0Fdlmimspw6d7MPbSM7w9p76V4c03T5SDJbW0cLkdCVUA4/KitHtRXpJWVkYn//2Q=='}],
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   'markdown': '![img-3.jpeg](img-3.jpeg)\n\nFigure 4: Performance of Mistral 7B and different Llama models on a wide range of benchmarks. All models were re-evaluated on all metrics with our evaluation pipeline for accurate comparison. Mistral 7B significantly outperforms Llama 2 7B and Llama 2 13B on all benchmarks. It is also vastly superior to Llama 1 34B in mathematics, code generation, and reasoning benchmarks.\n\n| Model | Modality | MMLU | HellaSwag | WinoG | PIQA | Arc-e | Arc-c | NQ | TriviaQA | HumanEval | MBPP | MATH | GSM8K |\n| :-- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n| LLaMA 2 7B | Pretrained | $44.4 \\%$ | $77.1 \\%$ | $69.5 \\%$ | $77.9 \\%$ | $68.7 \\%$ | $43.2 \\%$ | $24.7 \\%$ | $63.8 \\%$ | $11.6 \\%$ | $26.1 \\%$ | $3.9 \\%$ | $16.0 \\%$ |\n| LLaMA 2 13B | Pretrained | $55.6 \\%$ | $\\mathbf{8 0 . 7 \\%}$ | $72.9 \\%$ | $80.8 \\%$ | $75.2 \\%$ | $48.8 \\%$ | $\\mathbf{2 9 . 0 \\%}$ | $\\mathbf{6 9 . 6 \\%}$ | $18.9 \\%$ | $35.4 \\%$ | $6.0 \\%$ | $34.3 \\%$ |\n| Code-Llama 7B | Finetuned | $36.9 \\%$ | $62.9 \\%$ | $62.3 \\%$ | $72.8 \\%$ | $59.4 \\%$ | $34.5 \\%$ | $11.0 \\%$ | $34.9 \\%$ | $\\mathbf{3 1 . 1 \\%}$ | $\\mathbf{5 2 . 5 \\%}$ | $5.2 \\%$ | $20.8 \\%$ |\n| Mistral 7B | Pretrained | $\\mathbf{6 0 . 1 \\%}$ | $\\mathbf{8 1 . 3 \\%}$ | $\\mathbf{7 5 . 3 \\%}$ | $\\mathbf{8 3 . 0 \\%}$ | $\\mathbf{8 0 . 0 \\%}$ | $\\mathbf{5 5 . 5 \\%}$ | $\\mathbf{2 8 . 8 \\%}$ | $\\mathbf{6 9 . 9 \\%}$ | $\\mathbf{3 0 . 5 \\%}$ | $47.5 \\%$ | $\\mathbf{1 3 . 1 \\%}$ | $\\mathbf{5 2 . 2 \\%}$ |\n\nTable 2: Comparison of Mistral 7B with Llama. Mistral 7B outperforms Llama 2 13B on all metrics, and approaches the code performance of Code-Llama 7B without sacrificing performance on non-code benchmarks.\n\nSize and Efficiency. We computed "equivalent model sizes" of the Llama 2 family, aiming to understand Mistral 7B models\' efficiency in the cost-performance spectrum (see Figure 5). When evaluated on reasoning, comprehension, and STEM reasoning (specifically MMLU), Mistral 7B mirrored performance that one might expect from a Llama 2 model with more than 3x its size. On the Knowledge benchmarks, Mistral 7B\'s performance achieves a lower compression rate of 1.9 x , which is likely due to its limited parameter count that restricts the amount of knowledge it can store.\n\nEvaluation Differences. On some benchmarks, there are some differences between our evaluation protocol and the one reported in the Llama 2 paper: 1) on MBPP, we use the hand-verified subset 2) on TriviaQA, we do not provide Wikipedia contexts.\n\n## 4 Instruction Finetuning\n\nTo evaluate the generalization capabilities of Mistral 7B, we fine-tuned it on instruction datasets publicly available on the Hugging Face repository. No proprietary data or training tricks were utilized: Mistral 7B - Instruct model is a simple and preliminary demonstration that the base model can easily be fine-tuned to achieve good performance. In Table 3, we observe that the resulting model, Mistral 7B - Instruct, exhibits superior performance compared to all 7B models on MT-Bench, and is comparable to 13B - Chat models. An independent human evaluation was conducted on https://limboxing.com/leaderboard.\n\n| Model | Chatbot Arena <br> ELO Rating | MT Bench |\n| :-- | :--: | :--: |\n| WizardLM 13B v1.2 | 1047 | 7.2 |\n| Mistral 7B Instruct | $\\mathbf{1 0 3 1}$ | $\\mathbf{6 . 8 4}$ +/- $\\mathbf{0 . 0 7}$ |\n| Llama 2 13B Chat | 1012 | 6.65 |\n| Vicuna 13B | 1041 | 6.57 |\n| Llama 2 7B Chat | 985 | 6.27 |\n| Vicuna 7B | 997 | 6.17 |\n| Alpaca 13B | 914 | 4.53 |\n\nTable 3: Comparison of Chat models. Mistral 7B Instruct outperforms all 7B models on MT-Bench, and is comparable to 13B - Chat models.\n\nIn this evaluation, participants were provided with a set of questions along with anonymous responses from two models and were asked to select their preferred response, as illustrated in Figure 6. As of October 6, 2023, the outputs generated by Mistral 7B were preferred 5020 times, compared to 4143 times for Llama 2 13B.',
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  {'index': 4,
   'markdown': '![img-4.jpeg](img-4.jpeg)\n\nFigure 5: Results on MMLU, commonsense reasoning, world knowledge and reading comprehension for Mistral 7B and Llama 2 (7B/13B/70B). Mistral 7B largely outperforms Llama 2 13B on all evaluations, except on knowledge benchmarks, where it is on par (this is likely due to its limited parameter count, which limits the amount of knowledge it can compress).\n\n# 5 Adding guardrails for front-facing applications \n\nThe ability to enforce guardrails when it comes to AI generation is important for front-facing applications. In this section, we highlight how to leverage system prompting to optionally enforce output constraints on top of our models. Additionally, we showcase the ability of Mistral 7B to perform fine-grained content moderation, which can be useful to enforce quality content in applications.\n\n### 5.1 System prompt to enforce guardrails\n\nWe introduce a system prompt (see below) to guide the model to generate answers within specified guardrails, similar to the work done with Llama 2. Using this prompt allows the user to move on the Pareto front of model utility / guardrails enforcement, as indicated in Table 4.\n\nAlways assist with care, respect, and truth. Respond with utmost utility yet securely. Avoid harmful, unethical, prejudiced, or negative content. Ensure replies promote fairness and positivity.\n\nWe use a set of 175 unsafe prompts for evaluating safety. With the recommended system prompt, the model properly declines to answer $100 \\%$ of the harmful questions.\nAs an illustration, we provide in Table 5 the answers of both Mistral 7B - Instruct and Llama 2 Chat 13B to the question How to kill a linux process with system prompts activated. We observe that Mistral 7B provides a correct response while Llama 2 declines to answer. Note that on this specific question, both models answer correctly when system prompts are deactivated.\n\n| Guardrails | MT Bench |\n| :-- | :--: |\n| No system prompt | $6.84 \\pm 0.07$ |\n| Llama 2 system prompt | $6.38 \\pm 0.07$ |\n| Mistral system prompt | $6.58 \\pm 0.05$ |\n\nTable 4: System prompts. Mean official MT Bench score over 10 iterations with standard deviation for Mistral 7B - Instruct. For reference, Llama 2 13B - Chat reports official results of 6.65 .\n\n### 5.2 Content moderation with self-reflection\n\nMistral 7B - Instruct can be used as a content moderator: the model itself is able to accurately classify a user prompt or its generated answer as being either acceptable or falling into one of the following categories: Illegal activities such as terrorism, child abuse or fraud; Hateful, harassing or violent content such as discrimination, self-harm or bullying; Unqualified advice for instance in legal, medical or financial domains.',
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CiiigAooooAKKKKACiiigAooooAKKKKACiiigArG8TaRNrWiSWttKsV0rpNBIwyFkRgy59sitmigDhZ9R8cahbSad/wi9rZyyoYnvmv1eJQerKgG49TgHFddpdgumaXa2KEstvGsYY98CrlFAHjOk3uuad4V1/xQ39muttqN1Jc288G55wshDAvn5CAAFUDHA65rqNNjt9S8c+K5LyFX22dosCzKDtiZHJwD0y2c/QVtXfgbQr6/lu57WUmeUTTwLcSLBM46M8QYIxyByRzjmptX8I6Trd0tzdwzrOIjA0lvcyQmSMnOx9hG5evB9TQBl+BZbxvhxprQBJJBG4j81iB5YdgvI5+6BW54b3f8I7ZbgA3l8gdOpq9BbRWdmltbxrFBEmxEUcKAMAD2qn4d/wCQBZ/7n9TQBqUUlG4CgBjyrEhd2CqoyxPQCufs0bXr8X84P2GFv9GjPRz/AHz/AEpb2R9d1FtOgYiyhObqQfxn/nmP61vRxpFGERQqqMBR0AqdzX+HHzf4DwvHSlxS0VRkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ8j241yFDETcGBir5425AIq/kVQkW2/tuF2kcXQgYKv8JXIyfrnFYui2xlxM2mmRhcyf6QZ+mJGx8ue1AHU5GetLmuMmtrtrS+nM1sXkuvKdvs7biNwGM7/u+1XZNSuLOSWztEhQWqgbY7KRlkbbnA28IO3JJoA6bIpMj1rGS9vr+7kitfKthFGjSefEXbcwyFxkYAH41FEb06tqm+4ja3jCHyjGehXPB3ce/HPtQBvbhjOeKNwHesG21Vra0t/NjjjgayWWMJnqAMrkn3GO9RyaverJHbjZDKIBJIRbSSgE9FwvTHqaAOjpMiq1ncSXWnxTyxNC7pkowIIP0NZ+kOzeELZ2ZixtMkk852560AbWaMisqwuPJ8NWk8kiri1jYu4z/COTWf8A2pcywX1tMS3+ivLG/wBneDtjADEn8aAOkyPWjIrh7qac6BLpYkkDwxNMz7jkxhQV5+pUf8BNXtevI5JLexa9S1aOBrncz7cuBiMH2zkn6UAdXmiqmn3i6hp9vdJx5qBiP7p7j8DVugAooooAKKKKACiiigAooooAKRmVVLMQABkk9qWqeo6fFqmmXVhcbxFcxNE5RtpAIwcHtQBxVj4n1XUviDpqxSBNBvre5+zxFBulEWwecTjIDFyAM9AD3qoniC8XxJqEereItQ06CPUmht4/sCG3aPC7QZSh5JyPvVfHw8Nt4r0PULXUb5rPT4pVKSzglc+XtVePunaQR6Yqz4i07xN4itbrRmtbG3sZ5CpuvNLMIt3BC4+9t/WgDb8S3tta6JOJ50jMo2puONxz0Falv/x7Rf7g/lWfrEfleH7lASQsYAJPJxjr/jWhB/x7Rf7g/lQBJuA70jyIilnYKo6k9q8n8XavqJ8QXEJnlijiYCNVYgY9a6Pw1ZT6/pUV1qtxNLGp2pETtVgOhOOtZqpeTjY7J4RwpKpKWjNqfxCkkrQaXA99OOpThF+rdKjGjXupndrF0fK6/ZbclU/4Eepragto7aNY4Y1RF6KowBU31qrN7nP7RLSCK9taQ2kCw28KRRjoqDAqwBx0paKozeoUmKWigAooooAzZ5ZhrtrEGbymhkLAfdyMYzTRpUyXU88F/LEJpA7IEUjPTvn0qSW5lTWrW2BHlyROzcc5GKqx6hcf2o0M7LChkKIjRn5x7NQBal0yOeK+VpGxeKEcjt8u3iifTTJcJcwXDQzqnllgAwdeuCKzVvZo9JjltmjR2ZlSMLuLtnjFLLqd9HcR2bFFnSBZJnWMuMkkYGPoaAL8umyyNDMt263USlDIVBDqeoI4GOmPpTjp7faobkXUgmRNjnauJBnoeOKl0+4kurCKWaNopWHzIRgg1Q0+FbbXb+JGfy/KjbDMTyS3PP0oAkl0SGbTPsJmkUBmcOPvAnOf5mpJtMDeQ1rKYJIFMaMoBG04yCOhHA/Ks66hFpq1nJHE8MbTYM3mlg+RwpH1rNnEqw3915MimK8Y/ahL/q1EmT8vegDrreKSKILLMZnB5cgD+QqjBpclvNK0OoSLFJK0jR7FIyeTzip9WuXtdHu7iP78cLMv1Aqhq9kkXh04eTdbxZRg5HOOp9aAJ4dHkgRolv5fs7FsxbFxhs8Zx70lto/k3NvLJeSS/ZgyxoQAACPYcn3rUi/1SZ64Ga5OR7izktXkjuBeteqskhJ2MjHp1xjB/SgDoLPTLa0itlCb3twVjkYfMATzVe30Gzi8gSYuFt4fJRZVDAcjn68CoX1S5Gtf2Xtj8xnEgk7CLqR/vf8A66s27tFr95BkmN4o5wP7rZZT+e0UAWLSwisZJzACElYNsHAU9OKuUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFGaACiiigAooooAKKKKAGv9xvoazfDv/IAs/8Ac/qa0n+430NZvh7jQLP/AHP6mgDTLADk1h6vfzPOml6e3+mTLln7Qp/ePv6VyfxA1G/h1KG2SSSK38vcpU43Hvk+1Xvh3JNPBfSSgv8AOuJm5LcEYz6DAx9ay57y5TtWFcKPt2zr9PsItNs0toF+VepPVj3J9zVog56U6itTibu7sKKKKACiiigAooooAKKKKACiiigAooooAKKKKAM+SCM63DcGYB1hZRF3IJHNRw6PDbOWjuLlEDl/LEvy5JyePxp8tqW1yC63phIHTb/EckfpVC3vLpJWiv5HWd0crGYx5bYGflP+NAGkLC0kt2Rfmjll844bq3X+lJNpkctw06SzwyOMOYnxu7cj+tZRnupbGwSzuSlxNCh8tVG1Rjlj6CknvtRa6uobfzS1sFVQsYIc7c5Y5oA1J9Mheb7QJp4HCBHeOTbuUf3v8aUWVrNdvdJK28qEfZJw2MjkVYO2Wz/0hQqtH84Y8DjkVk6Zbxy6nJe2kCwWfleUoVdvmnOd2B29/c0AW5NJsbm2trdxvS1xs+bpjjn8qfcWdvd3AlSd45wNu6GTBI9CKp6ZBFa6nqsUKbEHl4Ue6k8Vj2cTRWumXLQQpH9owZY/vtkkAH25oA65UWCDYXbaq8s7ZOPc1m2+k2dnHHEl3OIguFief5cdOlSa6SbBI+Qks8UTkf3WdQfz6fjTdYsxPbO0dpazyhCpM3Ye3BoAWPRLZIPI866eDZ5YjaYlduOlKmkWiSSb5ZZZZYTExkk3Er3x6VlyapLY6Rp09s5mWSALscfN04c/Sr1wXS40qXzPNkZyjOOjArkmgC/Jp9vLaPbNH+7eIxHHXbjGM06Cyit5ppo1O+bbvJP90YH04qzRQBXtbSO0V0iXarSNIR2BJycfjzViiigAooooAKKKKACiiigAooooAKKKKACiiigDP1mKWfR7qKGMySMmFUEDcfxqtFq10kMaHQtSyFAP+p9P+ulbFFAHKarPZXMkEmoeGr5nZxGjMsOST0H+srSi1KWCJY4tA1JY1GFVRDgD/v5VLxDreq6StxcJoEN3YWkZna5kvljwFXJIUqTxzzmtTQtRk1jQrHU5bRrV7qFZvJdtxQMMgE/Sgbk2km9CGXXZoYzI+h6oFHXAiP8AKSnf2xcf9AHU/wDyD/8AHK1sUUCMd9dmjaNW0LVMyNtXiI5OCf8Anp6A0/8Ati5/6AWp/wDkH/45Wpjpx0paAMr+2Ln/AKAWp/8AkH/45R/bFz/0AtT/APIP/wAcrVooAyv7Yuf+gFqf/kH/AOOUf2zcf9ALU/8AyD/8crVrE8SazeaFpcl7aaRLqPlqzyqkyRiNFXcWJY89OgBNAEEniC4XWLe2/sG/y8Uj8iLdhSoOP3nT5hSGVmuVnbRtYZlbeqNLEVU+oXzKsw6lPfeG7TWrDTPPubi2jlitzKqEBwrY3ngADGeOcVX0bxHdX2s3Oj6lpbaffxQicBbgTI6E4yGAHOexFAETojeV/wASLWFMQYIVkjGM8nH7ynyM0hjf+xtZWWNNglWSIOR1wT5nNdJRQBjQ6nPDEsa6HqpCjALGEn8T5lVIj5N612miaz5zcEmSMjH08z3rpKKAObVmFws7aLrMpUllWSSNgp9h5n1qD9xNLOh0LWCGffJGXj2Fm55HmYNdXSYwelAGPNqUs8EkMmgak0brtYfueQeP+elUpHluNOis7rRtXkVF2kq0SbsDviWumooAwI7qSNIV/sPVyITlCXjz+P73mmCVvtQuG0TWHcNuVXeMqp9h5ldFRQBzpdTnPh7VNxl87fmLdu+vmenGPShNSltbq4upNF1RnndEyFiOAOFH+s9SfzroqQjigDL/ALZuP+gFqf8A5B/+OVFP4ha2VGm0XUkDsEXIi5J7f6ytC/mubeykls7P7XcAfLEJAm//AIEelYmleIZ77W5NG1fSfsF8kK3MaicTo6ZIyCAMEEdxQBf/ALZuP+gFqf8A5B/+OUv9sXP/AEAtT/8AIP8A8crNj8ZW83jdfDcNtI/7uRmuww2CRApZB6kB1z6E46g109AGONdmaV4xoeqb0AJGIuhzj/lp7Gn/ANsXP/QC1P8A8g//ABytXHOcUUAZX9sXP/QC1P8A8g//ABymS67NDE8j6HqgVFLMQIjwOvAkrYoxxigDK/ti5/6AWp/+Qf8A45R/bFz/ANALU/8AyD/8crVooAx5NdmiUF9C1TBIAwIjyenSSn/2xc/9ALU//IP/AMcrUwSKWgDK/tm4/wCgFqf/AJB/+OVDD4ia48zytF1NvLco/EXDDqP9Z71meJPFWs+HLe8vn8Oxzafb4xOL9VZ8nAwmzPUjjNS33iO7try303TdFFzqc1ubueEzrEsI4By+DlieOnOKANT+2Lj/AKAWp/8AkH/45WlGxeNXKMpIyVbGR+VZ+g61Dr+kRahBG8YfKtG/3o3BwVP41qUAFFFFABRRRQAUUUUANYEqQPQ1z2lXt7Y6Xb2smh6iXjXaSvlYPJ6fvK6OkoA5rVr+2lsnn1Lw7fNBCpdmdYTtA6ni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  {'index': 5,
   'markdown': "| Model | Answer |\n| :--: | :--: |\n| Mistral 7B - Instruct with Mistral system prompt | To kill a Linux process, you can use the `kill' command followed by the process ID (PID) of the process you want to terminate. For example, to kill process with PID 1234, you would run the command `kill 1234`. It's important to note that killing a process can have unintended consequences, so it's generally a good idea to only kill processes that you are certain you want to terminate. Additionally, it's generally a good idea to use the 'kill' command with caution, as it can be used to terminate critical system processes that are necessary for the operation of the system. |\n| Llama 2 13B - Chat with Llama 2 system prompt | As a helpful and respectful assistant, I cannot provide information on how to kill a Linux process. It is not appropriate or ethical to intentionally harm or terminate processes, as it can cause damage to the system and potentially result in data loss. Additionally, it is important to respect the autonomy and security of other users' processes, as they may be critical to their work or personal activities. Instead, I suggest that you explore other options to address any issues or concerns you may have. |\n\nTable 5: Comparison between Mistral and Llama system prompts. Mistral provides the right answer whereas Llama 2 declines to answer to the question How to kill a linux process.\n\nTo do so, we designed a self-reflection prompt that makes Mistral 7B classify a prompt or a generated answer. We evaluated self-reflection on our manually curated and balanced dataset of adversarial and standard prompts and got a precision of $99.4 \\%$ for a recall of $95.6 \\%$ (considering acceptable prompts as positives).\n\nThe use cases are vast, from moderating comments on social media or forums to brand monitoring on the internet. In particular, the end user is able to select afterwards which categories to effectively filter based on their particular use-case.\n\n# 6 Conclusion \n\nOur work on Mistral 7B demonstrates that language models may compress knowledge more than what was previously thought. This opens up interesting perspectives: the field has so far put the emphasis on scaling laws in 2 dimensions (directly associating model capabilities to training cost, as in [14]); the problem is rather 3 dimensional (model capabilities, training cost, inference cost), and much remains to be explored to obtain the best performance with the smallest possible model.\n\n## Acknowledgements\n\nWe are grateful to CoreWeave for their 24/7 help in marshalling our cluster. We thank the CINECA/EuroHPC team, and in particular the operators of Leonardo, for their resources and help. We thank the maintainers of FlashAttention, vLLM, xFormers, Skypilot for their precious assistance in implementing new features and integrating their solutions into ours. A huge thanks to Tri Dao and Daniel Haziza for helping include Mistral related changes to FlashAttention and xFormers on a tight schedule. We thank the teams of Hugging Face, AWS, GCP, Azure ML for their intense help in making our model compatible everywhere.",
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  {'index': 6,
   'markdown': '# Can you recommend a foundational and accessible book about quantum physics? \n\nCapy Link to Question Read Round (Enter) $\\rightarrow$\n\nRound Winner: mistral-7b-instruct-v0.1\n![img-5.jpeg](img-5.jpeg)\n\n## mistral-7b-instruct-v0.1 $\\gg$ Round Winner $\\gg$\n\nA great book for a foundational understanding of quantum physics is "The Quantum Universe: Everything that Can Happen Does Happen" by Brian Cox and Jeff Forshaw. This book is accessible and easy to understand, making it a great choice for beginners. It covers all the basics of quantum mechanics, including superposition, entanglement, and the uncertainty principle. The authors also explore the implications of quantum physics for our understanding of the universe, including the nature of time and space. Overall, "The Quantum Universe" is an excellent choice for anyone looking to gain a foundational understanding of this fascinating field\n\n## llama-2-13b-chat\n\nCertainly! For a foundational and accessible introduction to quantum physics, I highly recommend "The Feynman Lectures on Physics" by Richard Feynman. This classic text provides a clear and engaging overview of the principles and concepts of quantum mechanics, with minimal mathematical formalism and plenty of illustrations and examples. It\'s a great starting point for anyone looking to gain a deeper understanding of quantum physics without getting bogged down in too much technical detail.\n\nFigure 6: Human evaluation of Mistral 7B - Instruct vs Llama 2 13B - Chat Example. An example of human evaluation from libboxing.com. The question asks for recommendations of books in quantum physics. Llama 2 13B - Chat recommends a general physics book, while Mistral 7B - Instruct recommends a more relevant book on quantum physics and describes in the contents in more detail.',
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  {'index': 8,
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