remove capital letters

Author: Your Name

content/learning-paths/servers-and-cloud-computing/tune-pytorch-cpu-perf-with-threads/build.md

@@ -58,7 +58,7 @@ aarch64_pytorch ~>
 
 Create a new `read` token on HuggingFace by clicking [this link](https://huggingface.co/settings/tokens/new?tokenType=read). 
 
-![hf-token](./HF-access-token.jpg)
+![hf-token](./hf-access-token.jpg)
 
 Provide a suitable token name, press create token and copy the generated token value. From within docker container, enter the following command and paste the token to login.
 

…hput vs OMP_NUM_THREADS (270M vs 1B).png …perf-with-threads/prefill_throughput.pngcontent/learning-paths/servers-and-cloud-computing/tune-pytorch-cpu-perf-with-threads/Prefill Throughput vs OMP_NUM_THREADS (270M vs 1B).png renamed to content/learning-paths/servers-and-cloud-computing/tune-pytorch-cpu-perf-with-threads/prefill_throughput.png content/learning-paths/servers-and-cloud-computing/tune-pytorch-cpu-perf-with-threads/Prefill Throughput vs OMP_NUM_THREADS (270M vs 1B).png renamed to content/learning-paths/servers-and-cloud-computing/tune-pytorch-cpu-perf-with-threads/prefill_throughput.png

@@ -102,7 +102,7 @@ Decode Tokens per second: 45.23
 
 The graph above shows how prefill tokens per second change with the number of OpenMP threads for the 270M and 1B variants of Gemma-3. As expected, the smaller 270M model runs faster. Both models reach their optimal token generation rate at around 16–32 threads, though the 270M model exhibits a sharper performance drop-off beyond this range compared with the 1B variant.
 
-![comparison](./Prefill%20Throughput%20vs%20OMP_NUM_THREADS%20(270M%20vs%201B).png)
+![comparison](./prefill_throughput.png)