147 lines
6.1 KiB
Markdown
147 lines
6.1 KiB
Markdown
---
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base_model: simplescaling/s1.1-7B
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library_name: transformers
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model_name: Qwen2.5-14B-Instruct-20250308_215306
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tags:
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- generated_from_trainer
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- trl
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- sft
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- TensorBlock
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- GGUF
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licence: license
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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[](https://tensorblock.co)
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[](https://twitter.com/tensorblock_aoi)
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[](https://discord.gg/Ej5NmeHFf2)
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[](https://github.com/TensorBlock)
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[](https://t.me/TensorBlock)
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## simplescaling/s1.1-7B - GGUF
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This repo contains GGUF format model files for [simplescaling/s1.1-7B](https://huggingface.co/simplescaling/s1.1-7B).
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).
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## Our projects
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<table border="1" cellspacing="0" cellpadding="10">
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<tr>
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<th colspan="2" style="font-size: 25px;">Forge</th>
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</tr>
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<tr>
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<th colspan="2">
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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
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</th>
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</tr>
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<tr>
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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
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</tr>
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<tr>
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<th colspan="2">
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<a href="https://github.com/TensorBlock/forge" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">🚀 Try it now! 🚀</a>
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</th>
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</tr>
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<tr>
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<th style="font-size: 25px;">Awesome MCP Servers</th>
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<th style="font-size: 25px;">TensorBlock Studio</th>
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</tr>
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<tr>
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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
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</tr>
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<tr>
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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</tr>
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<tr>
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<th>
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<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">👀 See what we built 👀</a>
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</th>
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<th>
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<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">👀 See what we built 👀</a>
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</th>
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</tr>
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</table>
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## Prompt template
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```
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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## Model file specification
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [s1.1-7B-Q2_K.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
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| [s1.1-7B-Q3_K_S.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
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| [s1.1-7B-Q3_K_M.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
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| [s1.1-7B-Q3_K_L.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
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| [s1.1-7B-Q4_0.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [s1.1-7B-Q4_K_S.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
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| [s1.1-7B-Q4_K_M.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
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| [s1.1-7B-Q5_0.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [s1.1-7B-Q5_K_S.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
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| [s1.1-7B-Q5_K_M.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
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| [s1.1-7B-Q6_K.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
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| [s1.1-7B-Q8_0.gguf](https://huggingface.co/tensorblock/s1.1-7B-GGUF/blob/main/s1.1-7B-Q8_0.gguf) | Q8_0 | 8.099 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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### Command line
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Firstly, install Huggingface Client
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```shell
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
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```shell
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huggingface-cli download tensorblock/s1.1-7B-GGUF --include "s1.1-7B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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```
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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```shell
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huggingface-cli download tensorblock/s1.1-7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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