Upload folder using huggingface_hub

.gitattributes

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README.md

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+---
+library_name: transformers
+datasets:
+- CyberNative/github_cybersecurity_READMEs
+tags:
+- TensorBlock
+- GGUF
+base_model: Miti-H/distilgpt2-finetuned-Cybersecurity
+---
+
+<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>
+
+## Miti-H/distilgpt2-finetuned-Cybersecurity - GGUF
+
+This repo contains GGUF format model files for [Miti-H/distilgpt2-finetuned-Cybersecurity](https://huggingface.co/Miti-H/distilgpt2-finetuned-Cybersecurity).
+
+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).
+
+<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
+
+```
+
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [distilgpt2-finetuned-Cybersecurity-Q2_K.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q2_K.gguf) | Q2_K | 0.065 GB | smallest, significant quality loss - not recommended for most purposes |
+| [distilgpt2-finetuned-Cybersecurity-Q3_K_S.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q3_K_S.gguf) | Q3_K_S | 0.072 GB | very small, high quality loss |
+| [distilgpt2-finetuned-Cybersecurity-Q3_K_M.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q3_K_M.gguf) | Q3_K_M | 0.075 GB | very small, high quality loss |
+| [distilgpt2-finetuned-Cybersecurity-Q3_K_L.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q3_K_L.gguf) | Q3_K_L | 0.078 GB | small, substantial quality loss |
+| [distilgpt2-finetuned-Cybersecurity-Q4_0.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q4_0.gguf) | Q4_0 | 0.082 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [distilgpt2-finetuned-Cybersecurity-Q4_K_S.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q4_K_S.gguf) | Q4_K_S | 0.082 GB | small, greater quality loss |
+| [distilgpt2-finetuned-Cybersecurity-Q4_K_M.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q4_K_M.gguf) | Q4_K_M | 0.085 GB | medium, balanced quality - recommended |
+| [distilgpt2-finetuned-Cybersecurity-Q5_0.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q5_0.gguf) | Q5_0 | 0.093 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [distilgpt2-finetuned-Cybersecurity-Q5_K_S.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q5_K_S.gguf) | Q5_K_S | 0.093 GB | large, low quality loss - recommended |
+| [distilgpt2-finetuned-Cybersecurity-Q5_K_M.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q5_K_M.gguf) | Q5_K_M | 0.095 GB | large, very low quality loss - recommended |
+| [distilgpt2-finetuned-Cybersecurity-Q6_K.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q6_K.gguf) | Q6_K | 0.103 GB | very large, extremely low quality loss |
+| [distilgpt2-finetuned-Cybersecurity-Q8_0.gguf](https://huggingface.co/tensorblock/distilgpt2-finetuned-Cybersecurity-GGUF/blob/main/distilgpt2-finetuned-Cybersecurity-Q8_0.gguf) | Q8_0 | 0.132 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/distilgpt2-finetuned-Cybersecurity-GGUF --include "distilgpt2-finetuned-Cybersecurity-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/distilgpt2-finetuned-Cybersecurity-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```

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