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Vikhr-7B-instruct_0.2-GGUF

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  1. .gitattributes +12 -0
  2. README.md +81 -0
  3. Vikhr-7B-instruct_0.2-Q2_K.gguf +3 -0
  4. Vikhr-7B-instruct_0.2-Q3_K_L.gguf +3 -0
  5. Vikhr-7B-instruct_0.2-Q3_K_M.gguf +3 -0
  6. Vikhr-7B-instruct_0.2-Q3_K_S.gguf +3 -0
  7. Vikhr-7B-instruct_0.2-Q4_0.gguf +3 -0
  8. Vikhr-7B-instruct_0.2-Q4_K_M.gguf +3 -0
  9. Vikhr-7B-instruct_0.2-Q4_K_S.gguf +3 -0
  10. Vikhr-7B-instruct_0.2-Q5_0.gguf +3 -0
  11. Vikhr-7B-instruct_0.2-Q5_K_M.gguf +3 -0
  12. Vikhr-7B-instruct_0.2-Q5_K_S.gguf +3 -0
  13. Vikhr-7B-instruct_0.2-Q6_K.gguf +3 -0
  14. Vikhr-7B-instruct_0.2-Q8_0.gguf +3 -0
.gitattributes CHANGED
@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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+ Vikhr-7B-instruct_0.2-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ language:
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+ - ru
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+ - en
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+ datasets:
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+ - zjkarina/Vikhr_instruct
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+ - dichspace/darulm
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+ base_model: Vikhrmodels/Vikhr-7B-instruct_0.2
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+ tags:
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+ - TensorBlock
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+ - GGUF
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+ ---
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+
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+ <div style="width: auto; margin-left: auto; margin-right: auto">
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+ <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+ </div>
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+ <div style="display: flex; justify-content: space-between; width: 100%;">
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+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
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+ <p style="margin-top: 0.5em; margin-bottom: 0em;">
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+ 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>
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+ </p>
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+ </div>
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+ </div>
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+
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+ ## Vikhrmodels/Vikhr-7B-instruct_0.2 - GGUF
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+
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+ This repo contains GGUF format model files for [Vikhrmodels/Vikhr-7B-instruct_0.2](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.2).
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+
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+ The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
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+
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+ <div style="text-align: left; margin: 20px 0;">
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+ <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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+ Run them on the TensorBlock client using your local machine ↗
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+ </a>
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+ </div>
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+
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+ ## Prompt template
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+
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+ ```
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+
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+ ```
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+
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+ ## Model file specification
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+
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+ | Filename | Quant type | File Size | Description |
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+ | -------- | ---------- | --------- | ----------- |
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+ | [Vikhr-7B-instruct_0.2-Q2_K.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q2_K.gguf) | Q2_K | 2.667 GB | smallest, significant quality loss - not recommended for most purposes |
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+ | [Vikhr-7B-instruct_0.2-Q3_K_S.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q3_K_S.gguf) | Q3_K_S | 3.094 GB | very small, high quality loss |
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+ | [Vikhr-7B-instruct_0.2-Q3_K_M.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q3_K_M.gguf) | Q3_K_M | 3.443 GB | very small, high quality loss |
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+ | [Vikhr-7B-instruct_0.2-Q3_K_L.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q3_K_L.gguf) | Q3_K_L | 3.743 GB | small, substantial quality loss |
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+ | [Vikhr-7B-instruct_0.2-Q4_0.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q4_0.gguf) | Q4_0 | 3.987 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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+ | [Vikhr-7B-instruct_0.2-Q4_K_S.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q4_K_S.gguf) | Q4_K_S | 4.018 GB | small, greater quality loss |
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+ | [Vikhr-7B-instruct_0.2-Q4_K_M.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q4_K_M.gguf) | Q4_K_M | 4.242 GB | medium, balanced quality - recommended |
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+ | [Vikhr-7B-instruct_0.2-Q5_0.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q5_0.gguf) | Q5_0 | 4.827 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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+ | [Vikhr-7B-instruct_0.2-Q5_K_S.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q5_K_S.gguf) | Q5_K_S | 4.827 GB | large, low quality loss - recommended |
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+ | [Vikhr-7B-instruct_0.2-Q5_K_M.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q5_K_M.gguf) | Q5_K_M | 4.958 GB | large, very low quality loss - recommended |
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+ | [Vikhr-7B-instruct_0.2-Q6_K.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q6_K.gguf) | Q6_K | 5.720 GB | very large, extremely low quality loss |
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+ | [Vikhr-7B-instruct_0.2-Q8_0.gguf](https://huggingface.co/tensorblock/Vikhr-7B-instruct_0.2-GGUF/blob/main/Vikhr-7B-instruct_0.2-Q8_0.gguf) | Q8_0 | 7.408 GB | very large, extremely low quality loss - not recommended |
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+
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+
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+ ## Downloading instruction
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+
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+ ### Command line
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+
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+ Firstly, install Huggingface Client
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+
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+ ```shell
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+ pip install -U "huggingface_hub[cli]"
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+ ```
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+
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+ Then, downoad the individual model file the a local directory
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+
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+ ```shell
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+ huggingface-cli download tensorblock/Vikhr-7B-instruct_0.2-GGUF --include "Vikhr-7B-instruct_0.2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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+ ```
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+
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+ If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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+
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+ ```shell
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+ huggingface-cli download tensorblock/Vikhr-7B-instruct_0.2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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+ ```
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