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.gitattributes

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README.md

@@ -0,0 +1,154 @@
+---
+license: apache-2.0
+datasets:
+- THU-KEG/LongWriter-Zero-RLData
+base_model: THU-KEG/LongWriter-Zero-32B
+tags:
+- reinforcement-learning
+- writing
+- Long Context
+- TensorBlock
+- GGUF
+language:
+- en
+- zh
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+<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>
+
+[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
+[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)
+[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)
+[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)
+
+
+## THU-KEG/LongWriter-Zero-32B - GGUF
+
+<div style="text-align: left; margin: 20px 0;">
+    <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
+        Join our Discord to learn more about what we're building ↗
+    </a>
+</div>
+
+This repo contains GGUF format model files for [THU-KEG/LongWriter-Zero-32B](https://huggingface.co/THU-KEG/LongWriter-Zero-32B).
+
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
+
+## Our projects
+<table border="1" cellspacing="0" cellpadding="10">
+  <tr>
+    <th colspan="2" style="font-size: 25px;">Forge</th>
+  </tr>
+  <tr>
+    <th colspan="2">
+      <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
+    </th>
+  </tr>
+  <tr>
+    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
+  </tr>
+  <tr>
+    <th colspan="2">
+      <a href="https://github.com/TensorBlock/forge" 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;
+      ">🚀 Try it now! 🚀</a>
+    </th>
+  </tr>
+
+  <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="MCP Servers" width="450"/></th>
+    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" 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
+
+```
+A conversation between the user and the assistant. The user provides a writing/general task, and the assistant completes it. The assistant first deeply thinks through the writing/answering process in their mind before providing the final written work to the user. The assistant should engage in comprehensive and in-depth planning to ensure that every aspect of the writing/general task is detailed and well-structured. If there is any uncertainty or ambiguity in the writing request, the assistant should reflect, ask themselves clarifying questions, and explore multiple writing approaches to ensure the final output meets the highest quality standards. Since writing is both a creative and structured task, the assistant should analyze it from multiple perspectives, considering coherence, clarity, style, tone, audience, purpose, etc.. Additionally, the assistant should review and refine the work to enhance its expressiveness. The writing thought process and the final written work should be enclosed within <think> </think> and <answer> </answer> tags, respectively, as shown below: <think>A comprehensive strategy for writing that encompasses detailed planning and structural design—including brainstorming, outlining, style selection, audience adaptation, self-reflection, quality assurance, etc..</think> <answer>The final written work after thorough optimization and refinement.</answer> <|user|>: {system_prompt} <|assistant|>:
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [LongWriter-Zero-32B-Q2_K.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q2_K.gguf) | Q2_K | 12.313 GB | smallest, significant quality loss - not recommended for most purposes |
+| [LongWriter-Zero-32B-Q3_K_S.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q3_K_S.gguf) | Q3_K_S | 14.392 GB | very small, high quality loss |
+| [LongWriter-Zero-32B-Q3_K_M.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q3_K_M.gguf) | Q3_K_M | 15.935 GB | very small, high quality loss |
+| [LongWriter-Zero-32B-Q3_K_L.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q3_K_L.gguf) | Q3_K_L | 17.247 GB | small, substantial quality loss |
+| [LongWriter-Zero-32B-Q4_0.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q4_0.gguf) | Q4_0 | 18.640 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [LongWriter-Zero-32B-Q4_K_S.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q4_K_S.gguf) | Q4_K_S | 18.784 GB | small, greater quality loss |
+| [LongWriter-Zero-32B-Q4_K_M.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q4_K_M.gguf) | Q4_K_M | 19.851 GB | medium, balanced quality - recommended |
+| [LongWriter-Zero-32B-Q5_0.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q5_0.gguf) | Q5_0 | 22.638 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [LongWriter-Zero-32B-Q5_K_S.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q5_K_S.gguf) | Q5_K_S | 22.638 GB | large, low quality loss - recommended |
+| [LongWriter-Zero-32B-Q5_K_M.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q5_K_M.gguf) | Q5_K_M | 23.262 GB | large, very low quality loss - recommended |
+| [LongWriter-Zero-32B-Q6_K.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q6_K.gguf) | Q6_K | 26.886 GB | very large, extremely low quality loss |
+| [LongWriter-Zero-32B-Q8_0.gguf](https://huggingface.co/tensorblock/THU-KEG_LongWriter-Zero-32B-GGUF/blob/main/LongWriter-Zero-32B-Q8_0.gguf) | Q8_0 | 34.821 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/THU-KEG_LongWriter-Zero-32B-GGUF --include "LongWriter-Zero-32B-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/THU-KEG_LongWriter-Zero-32B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```