purge HF_HUB_ENABLE_HF_TRANSFER; promote Xet

docs/hub/mlx.md

@@ -70,9 +70,9 @@ response = generate(model, tokenizer, prompt="hello", verbose=True)
 MLX-LM supports popular LLM architectures including LLaMA, Phi-2, Mistral, and Qwen. Models other than supported ones can easily be downloaded as follows:
 
 ```py
-pip install huggingface_hub hf_transfer
+pip install -U huggingface_hub
 
-export HF_HUB_ENABLE_HF_TRANSFER=1
+export HF_XET_HIGH_PERFORMANCE=1
 hf download --local-dir <LOCAL FOLDER PATH> <USER_ID>/<MODEL_NAME>
 ```
 

docs/hub/models-downloading.md

@@ -51,18 +51,9 @@ Add your SSH public key to [your user settings](https://huggingface.co/settings/
 ## Faster downloads
 
 If you are running on a machine with high bandwidth,
-you can increase your download speed with [`hf_transfer`](https://github.com/huggingface/hf_transfer),
-a Rust-based library developed to speed up file transfers with the Hub.
+you can speed up downloads by allowing `hf_xet` to run on all CPU cores. `hf_xet` is a Rust-based package leveraging the new [Xet storage backend](https://huggingface.co/docs/hub/en/xet/index) to optimize file transfers with chunk-based deduplication. `hf_xet` is enabled by default but with lower performances to avoid bloating available CPU and bandwidth, which could degrade UX.
 
 ```bash
-pip install "huggingface_hub[hf_transfer]"
-HF_HUB_ENABLE_HF_TRANSFER=1 hf download ...
+pip install -U huggingface_hub
+HF_XET_HIGH_PERFORMANCE=1 hf download ...
 ```
-
-> [!WARNING]
-> `hf_transfer` is a power user tool!
-> It is tested and production-ready,
-> but it lacks user-friendly features like advanced error handling or proxies.
-> For more details, please take a look at this [guide](https://huggingface.co/docs/huggingface_hub/hf_transfer).
-
-