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---
library_name: transformers
license: other
base_model: gtonkov/SpecAI
tags:
- llama-factory
- full
- generated_from_trainer
- TensorBlock
- GGUF
model-index:
- name: SpecAI
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>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## gtonkov/SpecAI - GGUF
This repo contains GGUF format model files for [gtonkov/SpecAI](https://huggingface.co/gtonkov/SpecAI).
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).
## 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
```
<s><|system|>
{system_prompt}<|end|>
<|user|>
{prompt}<|end|>
<|assistant|>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [SpecAI-Q2_K.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q2_K.gguf) | Q2_K | 1.319 GB | smallest, significant quality loss - not recommended for most purposes |
| [SpecAI-Q3_K_S.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q3_K_S.gguf) | Q3_K_S | 1.566 GB | very small, high quality loss |
| [SpecAI-Q3_K_M.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q3_K_M.gguf) | Q3_K_M | 1.821 GB | very small, high quality loss |
| [SpecAI-Q3_K_L.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q3_K_L.gguf) | Q3_K_L | 1.944 GB | small, substantial quality loss |
| [SpecAI-Q4_0.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q4_0.gguf) | Q4_0 | 2.027 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [SpecAI-Q4_K_S.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q4_K_S.gguf) | Q4_K_S | 2.038 GB | small, greater quality loss |
| [SpecAI-Q4_K_M.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q4_K_M.gguf) | Q4_K_M | 2.229 GB | medium, balanced quality - recommended |
| [SpecAI-Q5_0.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q5_0.gguf) | Q5_0 | 2.460 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [SpecAI-Q5_K_S.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q5_K_S.gguf) | Q5_K_S | 2.460 GB | large, low quality loss - recommended |
| [SpecAI-Q5_K_M.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q5_K_M.gguf) | Q5_K_M | 2.622 GB | large, very low quality loss - recommended |
| [SpecAI-Q6_K.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q6_K.gguf) | Q6_K | 2.920 GB | very large, extremely low quality loss |
| [SpecAI-Q8_0.gguf](https://huggingface.co/tensorblock/SpecAI-GGUF/blob/main/SpecAI-Q8_0.gguf) | Q8_0 | 3.782 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/SpecAI-GGUF --include "SpecAI-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/SpecAI-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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