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| Compression Speed vs Ratio | Decompression Speed |
| ---------------------------|-------------------- |
-| <img src="https://raw.githubusercontent.com/Cyan4973/zstd/master/images/Cspeed4.png" alt="Compression Speed vs Ratio" style="height:500px;"> | <img src="https://raw.githubusercontent.com/Cyan4973/zstd/master/images/Dspeed4.png" alt="Decompression Speed" style="height:500px;">
+| <img src="https://raw.githubusercontent.com/facebook/zstd/master/images/Cspeed4.png" alt="Compression Speed vs Ratio" style="height:500px;"> | <img src="https://raw.githubusercontent.com/facebook/zstd/master/images/Dspeed4.png" alt="Decompression Speed" style="height:500px;">
Several algorithms can produce higher compression ratio but at slower speed, falling outside of the graph.
-For a larger picture including very slow modes, [click on this link](https://raw.githubusercontent.com/Cyan4973/zstd/master/images/DCspeed5.png) .
+For a larger picture including very slow modes, [click on this link](https://raw.githubusercontent.com/facebook/zstd/master/images/DCspeed5.png) .
<a name="small-data"></a>
### The case for Small Data compression
To solve this situation, Zstd offers a __training mode__, which can be used to tune the algorithm for a selected type of data, by providing it with a few samples. The result of the training is stored in a file called "dictionary", which can be loaded before compression and decompression. Using this dictionary, the compression ratio achievable on small data improves dramatically :
-<img src="https://raw.githubusercontent.com/Cyan4973/zstd/master/images/smallData.png" alt="Compressing Small Data" style="height:600px;">
+<img src="https://raw.githubusercontent.com/facebook/zstd/master/images/smallData.png" alt="Compressing Small Data" style="height:600px;">
These compression gains are achieved while simultaneously providing faster compression and decompression speeds.