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 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+bloomz-7b1-mt-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+bloomz-7b1-mt-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+bloomz-7b1-mt-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+bloomz-7b1-mt-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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+bloomz-7b1-mt-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+bloomz-7b1-mt-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+bloomz-7b1-mt-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+bloomz-7b1-mt-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,724 @@
+---
+datasets:
+- bigscience/xP3mt
+license: bigscience-bloom-rail-1.0
+language:
+- ak
+- ar
+- as
+- bm
+- bn
+- ca
+- code
+- en
+- es
+- eu
+- fon
+- fr
+- gu
+- hi
+- id
+- ig
+- ki
+- kn
+- lg
+- ln
+- ml
+- mr
+- ne
+- nso
+- ny
+- or
+- pa
+- pt
+- rn
+- rw
+- sn
+- st
+- sw
+- ta
+- te
+- tn
+- ts
+- tum
+- tw
+- ur
+- vi
+- wo
+- xh
+- yo
+- zh
+- zu
+programming_language:
+- C
+- C++
+- C#
+- Go
+- Java
+- JavaScript
+- Lua
+- PHP
+- Python
+- Ruby
+- Rust
+- Scala
+- TypeScript
+pipeline_tag: text-generation
+widget:
+- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous
+    review as positive, neutral or negative?
+  example_title: zh-en sentiment
+- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
+  example_title: zh-zh sentiment
+- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
+  example_title: vi-en query
+- text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».
+  example_title: fr-fr query
+- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
+  example_title: te-en qa
+- text: Why is the sky blue?
+  example_title: en-en qa
+- text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon.
+    The fairy tale is a masterpiece that has achieved praise worldwide and its moral
+    is "Heroes Come in All Shapes and Sizes". Story (in Spanish):'
+  example_title: es-en fable
+- text: 'Write a fable about wood elves living in a forest that is suddenly invaded
+    by ogres. The fable is a masterpiece that has achieved praise worldwide and its
+    moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):'
+  example_title: hi-en fable
+tags:
+- TensorBlock
+- GGUF
+base_model: bigscience/bloomz-7b1-mt
+model-index:
+- name: bloomz-7b1-mt
+  results:
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: Winogrande XL (xl)
+      type: winogrande
+      config: xl
+      split: validation
+      revision: a80f460359d1e9a67c006011c94de42a8759430c
+    metrics:
+    - type: Accuracy
+      value: 56.51
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (en)
+      type: Muennighoff/xwinograd
+      config: en
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 65.76
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (fr)
+      type: Muennighoff/xwinograd
+      config: fr
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 57.83
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (jp)
+      type: Muennighoff/xwinograd
+      config: jp
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 51.82
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (pt)
+      type: Muennighoff/xwinograd
+      config: pt
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 57.41
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (ru)
+      type: Muennighoff/xwinograd
+      config: ru
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 55.87
+  - task:
+      type: Coreference resolution
+    dataset:
+      name: XWinograd (zh)
+      type: Muennighoff/xwinograd
+      config: zh
+      split: test
+      revision: 9dd5ea5505fad86b7bedad667955577815300cee
+    metrics:
+    - type: Accuracy
+      value: 62.7
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r1)
+      type: anli
+      config: r1
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 42.6
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r2)
+      type: anli
+      config: r2
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 39.4
+  - task:
+      type: Natural language inference
+    dataset:
+      name: ANLI (r3)
+      type: anli
+      config: r3
+      split: validation
+      revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
+    metrics:
+    - type: Accuracy
+      value: 42.0
+  - task:
+      type: Natural language inference
+    dataset:
+      name: SuperGLUE (cb)
+      type: super_glue
+      config: cb
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 83.93
+  - task:
+      type: Natural language inference
+    dataset:
+      name: SuperGLUE (rte)
+      type: super_glue
+      config: rte
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 82.67
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ar)
+      type: xnli
+      config: ar
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 55.58
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (bg)
+      type: xnli
+      config: bg
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 44.9
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (de)
+      type: xnli
+      config: de
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 48.92
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (el)
+      type: xnli
+      config: el
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 42.89
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (en)
+      type: xnli
+      config: en
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 58.92
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (es)
+      type: xnli
+      config: es
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 57.35
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (fr)
+      type: xnli
+      config: fr
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 56.67
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (hi)
+      type: xnli
+      config: hi
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 53.45
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ru)
+      type: xnli
+      config: ru
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 50.24
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (sw)
+      type: xnli
+      config: sw
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 48.27
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (th)
+      type: xnli
+      config: th
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 41.08
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (tr)
+      type: xnli
+      config: tr
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 38.71
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (ur)
+      type: xnli
+      config: ur
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 49.48
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (vi)
+      type: xnli
+      config: vi
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 54.5
+  - task:
+      type: Natural language inference
+    dataset:
+      name: XNLI (zh)
+      type: xnli
+      config: zh
+      split: validation
+      revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
+    metrics:
+    - type: Accuracy
+      value: 54.3
+  - task:
+      type: Program synthesis
+    dataset:
+      name: HumanEval
+      type: openai_humaneval
+      config: None
+      split: test
+      revision: e8dc562f5de170c54b5481011dd9f4fa04845771
+    metrics:
+    - type: Pass@1
+      value: 7.23
+    - type: Pass@10
+      value: 14.46
+    - type: Pass@100
+      value: 25.86
+  - task:
+      type: Sentence completion
+    dataset:
+      name: StoryCloze (2016)
+      type: story_cloze
+      config: '2016'
+      split: validation
+      revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
+    metrics:
+    - type: Accuracy
+      value: 89.58
+  - task:
+      type: Sentence completion
+    dataset:
+      name: SuperGLUE (copa)
+      type: super_glue
+      config: copa
+      split: validation
+      revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
+    metrics:
+    - type: Accuracy
+      value: 84.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (et)
+      type: xcopa
+      config: et
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 52.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (ht)
+      type: xcopa
+      config: ht
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 54.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (id)
+      type: xcopa
+      config: id
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 73.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (it)
+      type: xcopa
+      config: it
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 62.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (qu)
+      type: xcopa
+      config: qu
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 61.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (sw)
+      type: xcopa
+      config: sw
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 61.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (ta)
+      type: xcopa
+      config: ta
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 62.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (th)
+      type: xcopa
+      config: th
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 61.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (tr)
+      type: xcopa
+      config: tr
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 56.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (vi)
+      type: xcopa
+      config: vi
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 77.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XCOPA (zh)
+      type: xcopa
+      config: zh
+      split: validation
+      revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
+    metrics:
+    - type: Accuracy
+      value: 80.0
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (ar)
+      type: Muennighoff/xstory_cloze
+      config: ar
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 83.85
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (es)
+      type: Muennighoff/xstory_cloze
+      config: es
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 88.82
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (eu)
+      type: Muennighoff/xstory_cloze
+      config: eu
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 73.26
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (hi)
+      type: Muennighoff/xstory_cloze
+      config: hi
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 80.41
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (id)
+      type: Muennighoff/xstory_cloze
+      config: id
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 84.58
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (my)
+      type: Muennighoff/xstory_cloze
+      config: my
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 51.56
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (ru)
+      type: Muennighoff/xstory_cloze
+      config: ru
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 64.26
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (sw)
+      type: Muennighoff/xstory_cloze
+      config: sw
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 71.01
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (te)
+      type: Muennighoff/xstory_cloze
+      config: te
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 73.06
+  - task:
+      type: Sentence completion
+    dataset:
+      name: XStoryCloze (zh)
+      type: Muennighoff/xstory_cloze
+      config: zh
+      split: validation
+      revision: 8bb76e594b68147f1a430e86829d07189622b90d
+    metrics:
+    - type: Accuracy
+      value: 85.9
+---
+
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;">
+            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
+        </p>
+    </div>
+</div>
+
+## bigscience/bloomz-7b1-mt - GGUF
+
+This repo contains GGUF format model files for [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt).
+
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
+
+## Prompt template
+
+```
+
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [bloomz-7b1-mt-Q2_K.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q2_K.gguf) | Q2_K | 3.201 GB | smallest, significant quality loss - not recommended for most purposes |
+| [bloomz-7b1-mt-Q3_K_S.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q3_K_S.gguf) | Q3_K_S | 3.631 GB | very small, high quality loss |
+| [bloomz-7b1-mt-Q3_K_M.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q3_K_M.gguf) | Q3_K_M | 4.137 GB | very small, high quality loss |
+| [bloomz-7b1-mt-Q3_K_L.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q3_K_L.gguf) | Q3_K_L | 4.422 GB | small, substantial quality loss |
+| [bloomz-7b1-mt-Q4_0.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q4_0.gguf) | Q4_0 | 4.505 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [bloomz-7b1-mt-Q4_K_S.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q4_K_S.gguf) | Q4_K_S | 4.529 GB | small, greater quality loss |
+| [bloomz-7b1-mt-Q4_K_M.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q4_K_M.gguf) | Q4_K_M | 4.907 GB | medium, balanced quality - recommended |
+| [bloomz-7b1-mt-Q5_0.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q5_0.gguf) | Q5_0 | 5.328 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [bloomz-7b1-mt-Q5_K_S.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q5_K_S.gguf) | Q5_K_S | 5.328 GB | large, low quality loss - recommended |
+| [bloomz-7b1-mt-Q5_K_M.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q5_K_M.gguf) | Q5_K_M | 5.631 GB | large, very low quality loss - recommended |
+| [bloomz-7b1-mt-Q6_K.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q6_K.gguf) | Q6_K | 6.202 GB | very large, extremely low quality loss |
+| [bloomz-7b1-mt-Q8_0.gguf](https://huggingface.co/tensorblock/bloomz-7b1-mt-GGUF/tree/main/bloomz-7b1-mt-Q8_0.gguf) | Q8_0 | 8.028 GB | very large, extremely low quality loss - not recommended |
+
+
+## Downloading instruction
+
+### Command line
+
+Firstly, install Huggingface Client
+
+```shell
+pip install -U "huggingface_hub[cli]"
+```
+
+Then, downoad the individual model file the a local directory
+
+```shell
+huggingface-cli download tensorblock/bloomz-7b1-mt-GGUF --include "bloomz-7b1-mt-Q2_K.gguf" --local-dir MY_LOCAL_DIR
+```
+
+If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
+
+```shell
+huggingface-cli download tensorblock/bloomz-7b1-mt-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```

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