Upload folder using huggingface_hub

.gitattributes

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README.md

@@ -0,0 +1,85 @@
+---
+library_name: transformers
+model_name: Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO
+base_model: Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO
+language:
+- ru
+- en
+license: apache-2.0
+tags:
+- TensorBlock
+- GGUF
+---
+
+<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>
+
+## Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO - GGUF
+
+This repo contains GGUF format model files for [Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO](https://huggingface.co/Vikhrmodels/QVikhr-2.5-1.5B-Instruct-SMPO).
+
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).
+
+<div style="text-align: left; margin: 20px 0;">
+    <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;">
+        Run them on the TensorBlock client using your local machine ↗
+    </a>
+</div>
+
+## Prompt template
+
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q2_K.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q2_K.gguf) | Q2_K | 0.676 GB | smallest, significant quality loss - not recommended for most purposes |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_S.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_S.gguf) | Q3_K_S | 0.761 GB | very small, high quality loss |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_M.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_M.gguf) | Q3_K_M | 0.824 GB | very small, high quality loss |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_L.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q3_K_L.gguf) | Q3_K_L | 0.880 GB | small, substantial quality loss |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q4_0.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q4_0.gguf) | Q4_0 | 0.935 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_S.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_S.gguf) | Q4_K_S | 0.940 GB | small, greater quality loss |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_M.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q4_K_M.gguf) | Q4_K_M | 0.986 GB | medium, balanced quality - recommended |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q5_0.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q5_0.gguf) | Q5_0 | 1.098 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_S.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_S.gguf) | Q5_K_S | 1.098 GB | large, low quality loss - recommended |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_M.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q5_K_M.gguf) | Q5_K_M | 1.125 GB | large, very low quality loss - recommended |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q6_K.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q6_K.gguf) | Q6_K | 1.272 GB | very large, extremely low quality loss |
+| [QVikhr-2.5-1.5B-Instruct-SMPO-Q8_0.gguf](https://huggingface.co/tensorblock/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF/blob/main/QVikhr-2.5-1.5B-Instruct-SMPO-Q8_0.gguf) | Q8_0 | 1.646 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/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF --include "QVikhr-2.5-1.5B-Instruct-SMPO-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/QVikhr-2.5-1.5B-Instruct-SMPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```