Upload folder using huggingface_hub

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+open-llama-7b-v2-open-instruct-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,118 @@
+---
+license: cc-by-sa-3.0
+datasets:
+- VMware/open-instruct
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- TensorBlock
+- GGUF
+base_model: VMware/open-llama-7b-v2-open-instruct
+---
+
+<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>
+
+## VMware/open-llama-7b-v2-open-instruct - GGUF
+
+This repo contains GGUF format model files for [VMware/open-llama-7b-v2-open-instruct](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct).
+
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).
+
+## Our projects
+<table border="1" cellspacing="0" cellpadding="10">
+<tr>
+  <th style="font-size: 25px;">Awesome MCP Servers</th>
+  <th style="font-size: 25px;">TensorBlock Studio</th>
+</tr>
+  <tr>
+    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
+    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
+  </tr>
+  <tr>
+    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
+    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
+  </tr>
+<tr>
+  <th>
+    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
+      display: inline-block;
+      padding: 8px 16px;
+      background-color: #FF7F50;
+      color: white;
+      text-decoration: none;
+      border-radius: 6px;
+      font-weight: bold;
+      font-family: sans-serif;
+    ">👀 See what we built 👀</a>
+  </th>
+  <th>
+    <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
+      display: inline-block;
+      padding: 8px 16px;
+      background-color: #FF7F50;
+      color: white;
+      text-decoration: none;
+      border-radius: 6px;
+      font-weight: bold;
+      font-family: sans-serif;
+    ">👀 See what we built 👀</a>
+  </th>
+</tr>
+</table>
+
+## Prompt template
+
+```
+
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [open-llama-7b-v2-open-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q2_K.gguf) | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes |
+| [open-llama-7b-v2-open-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q3_K_S.gguf) | Q3_K_S | 2.948 GB | very small, high quality loss |
+| [open-llama-7b-v2-open-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q3_K_M.gguf) | Q3_K_M | 3.298 GB | very small, high quality loss |
+| [open-llama-7b-v2-open-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q3_K_L.gguf) | Q3_K_L | 3.597 GB | small, substantial quality loss |
+| [open-llama-7b-v2-open-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q4_0.gguf) | Q4_0 | 3.826 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [open-llama-7b-v2-open-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q4_K_S.gguf) | Q4_K_S | 3.857 GB | small, greater quality loss |
+| [open-llama-7b-v2-open-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q4_K_M.gguf) | Q4_K_M | 4.081 GB | medium, balanced quality - recommended |
+| [open-llama-7b-v2-open-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q5_0.gguf) | Q5_0 | 4.652 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [open-llama-7b-v2-open-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q5_K_S.gguf) | Q5_K_S | 4.652 GB | large, low quality loss - recommended |
+| [open-llama-7b-v2-open-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q5_K_M.gguf) | Q5_K_M | 4.783 GB | large, very low quality loss - recommended |
+| [open-llama-7b-v2-open-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q6_K.gguf) | Q6_K | 5.529 GB | very large, extremely low quality loss |
+| [open-llama-7b-v2-open-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/VMware_open-llama-7b-v2-open-instruct-GGUF/blob/main/open-llama-7b-v2-open-instruct-Q8_0.gguf) | Q8_0 | 7.161 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/VMware_open-llama-7b-v2-open-instruct-GGUF --include "open-llama-7b-v2-open-instruct-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/VMware_open-llama-7b-v2-open-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```

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