Upload folder using huggingface_hub

.gitattributes

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README.md

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+---
+base_model: princeton-nlp/gemma-2-9b-it-DPO
+tags:
+- alignment-handbook
+- generated_from_trainer
+- TensorBlock
+- GGUF
+datasets:
+- princeton-nlp/gemma2-ultrafeedback-armorm
+model-index:
+- name: princeton-nlp/gemma-2-9b-it-DPO
+  results: []
+---
+
+<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>
+
+## princeton-nlp/gemma-2-9b-it-DPO - GGUF
+
+This repo contains GGUF format model files for [princeton-nlp/gemma-2-9b-it-DPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-DPO).
+
+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
+
+```
+<bos><start_of_turn>user
+{system_prompt}
+
+{prompt}<end_of_turn>
+<start_of_turn>model
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [gemma-2-9b-it-DPO-Q2_K.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q2_K.gguf) | Q2_K | 3.544 GB | smallest, significant quality loss - not recommended for most purposes |
+| [gemma-2-9b-it-DPO-Q3_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q3_K_S.gguf) | Q3_K_S | 4.040 GB | very small, high quality loss |
+| [gemma-2-9b-it-DPO-Q3_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q3_K_M.gguf) | Q3_K_M | 4.435 GB | very small, high quality loss |
+| [gemma-2-9b-it-DPO-Q3_K_L.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q3_K_L.gguf) | Q3_K_L | 4.780 GB | small, substantial quality loss |
+| [gemma-2-9b-it-DPO-Q4_0.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q4_0.gguf) | Q4_0 | 5.069 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [gemma-2-9b-it-DPO-Q4_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q4_K_S.gguf) | Q4_K_S | 5.103 GB | small, greater quality loss |
+| [gemma-2-9b-it-DPO-Q4_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q4_K_M.gguf) | Q4_K_M | 5.365 GB | medium, balanced quality - recommended |
+| [gemma-2-9b-it-DPO-Q5_0.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q5_0.gguf) | Q5_0 | 6.038 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [gemma-2-9b-it-DPO-Q5_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q5_K_S.gguf) | Q5_K_S | 6.038 GB | large, low quality loss - recommended |
+| [gemma-2-9b-it-DPO-Q5_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q5_K_M.gguf) | Q5_K_M | 6.191 GB | large, very low quality loss - recommended |
+| [gemma-2-9b-it-DPO-Q6_K.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q6_K.gguf) | Q6_K | 7.068 GB | very large, extremely low quality loss |
+| [gemma-2-9b-it-DPO-Q8_0.gguf](https://huggingface.co/tensorblock/gemma-2-9b-it-DPO-GGUF/tree/main/gemma-2-9b-it-DPO-Q8_0.gguf) | Q8_0 | 9.152 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/gemma-2-9b-it-DPO-GGUF --include "gemma-2-9b-it-DPO-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/gemma-2-9b-it-DPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```

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