146 lines
9.1 KiB
Markdown
146 lines
9.1 KiB
Markdown
---
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base_model: Bin12345/AutoCoder_S_6.7B
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inference: false
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library_name: gguf
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license: apache-2.0
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pipeline_tag: text-generation
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quantized_by: legraphista
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tags:
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- quantized
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- GGUF
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- imatrix
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- quantization
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- imat
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- imatrix
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- static
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---
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# AutoCoder_S_6.7B-IMat-GGUF
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_Llama.cpp imatrix quantization of Bin12345/AutoCoder_S_6.7B_
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Original Model: [Bin12345/AutoCoder_S_6.7B](https://huggingface.co/Bin12345/AutoCoder_S_6.7B)
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Original dtype: `BF16` (`bfloat16`)
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Quantized by: llama.cpp [b3010](https://github.com/ggerganov/llama.cpp/releases/tag/b3010)
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IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw)
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- [AutoCoder_S_6.7B-IMat-GGUF](#autocoder-s-6-7b-imat-gguf)
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- [Files](#files)
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- [IMatrix](#imatrix)
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- [Common Quants](#common-quants)
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- [All Quants](#all-quants)
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- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
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- [Inference](#inference)
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- [Simple chat template](#simple-chat-template)
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- [Chat template with system prompt](#chat-template-with-system-prompt)
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- [Llama.cpp](#llama-cpp)
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- [FAQ](#faq)
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- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
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- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
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---
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## Files
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### IMatrix
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Status: ✅ Available
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Link: [here](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/imatrix.dat)
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### Common Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
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| [AutoCoder_S_6.7B.Q8_0.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q8_0.gguf) | Q8_0 | 7.16GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.Q6_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q6_K.gguf) | Q6_K | 5.53GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.Q4_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K.gguf) | Q4_K | 4.08GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.Q3_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K.gguf) | Q3_K | 3.30GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.Q2_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K.gguf) | Q2_K | 2.53GB | ✅ Available | 🟢 IMatrix | 📦 No
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### All Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
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| [AutoCoder_S_6.7B.BF16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.BF16.gguf) | BF16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.FP16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.FP16.gguf) | F16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.Q5_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K.gguf) | Q5_K | 4.79GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.Q5_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | ✅ Available | ⚪ Static | 📦 No
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| [AutoCoder_S_6.7B.Q4_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.Q3_K_L.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_L.gguf) | Q3_K_L | 3.60GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.Q3_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.Q2_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K_S.gguf) | Q2_K_S | 2.32GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ4_NL.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_NL.gguf) | IQ4_NL | 3.83GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ4_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_XS.gguf) | IQ4_XS | 3.62GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ3_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_M.gguf) | IQ3_M | 3.12GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ3_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_S.gguf) | IQ3_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ3_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XS.gguf) | IQ3_XS | 2.80GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ3_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XXS.gguf) | IQ3_XXS | 2.59GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ2_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_M.gguf) | IQ2_M | 2.36GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ2_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_S.gguf) | IQ2_S | 2.20GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ2_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XS.gguf) | IQ2_XS | 2.04GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ2_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XXS.gguf) | IQ2_XXS | 1.86GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ1_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_M.gguf) | IQ1_M | 1.65GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [AutoCoder_S_6.7B.IQ1_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_S.gguf) | IQ1_S | 1.53GB | ✅ Available | 🟢 IMatrix | 📦 No
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## Downloading using huggingface-cli
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If you do not have hugginface-cli installed:
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```
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pip install -U "huggingface_hub[cli]"
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```
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Download the specific file you want:
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```
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huggingface-cli download legraphista/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.Q8_0.gguf" --local-dir ./
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```
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If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
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```
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huggingface-cli download legraphista/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.Q8_0/*" --local-dir ./
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# see FAQ for merging GGUF's
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```
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---
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## Inference
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### Simple chat template
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```
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Human: Can you provide ways to eat combinations of bananas and dragonfruits?
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Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
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1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
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2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
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Human: What about solving an 2x + 3 = 7 equation?
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Assistant:
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```
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### Chat template with system prompt
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```
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You are a helpful AI.
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Human: Can you provide ways to eat combinations of bananas and dragonfruits?
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Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
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1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
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2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
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Human: What about solving an 2x + 3 = 7 equation?
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Assistant:
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```
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### Llama.cpp
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```
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llama.cpp/main -m AutoCoder_S_6.7B.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"
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```
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---
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## FAQ
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### Why is the IMatrix not applied everywhere?
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According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
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### How do I merge a split GGUF?
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1. Make sure you have `gguf-split` available
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- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
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- Download the appropriate zip for your system from the latest release
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- Unzip the archive and you should be able to find `gguf-split`
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2. Locate your GGUF chunks folder (ex: `AutoCoder_S_6.7B.Q8_0`)
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3. Run `gguf-split --merge AutoCoder_S_6.7B.Q8_0/AutoCoder_S_6.7B.Q8_0-00001-of-XXXXX.gguf AutoCoder_S_6.7B.Q8_0.gguf`
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- Make sure to point `gguf-split` to the first chunk of the split.
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---
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Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)! |