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Merge remote-tracking branch 'external/develop' into develop

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github-actions[bot]
2026-04-21 11:34:14 -04:00
parent 7ac7ca65e6 f544433d88
commit 535478332e
5 changed files with 8 additions and 8 deletions
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docs/how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference.rst
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@@ -127,7 +127,7 @@ Download the base model and fine-tuning dataset
.. code-block:: shell
huggingface-cli login
hf auth login
.. note::
docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-mori-distributed.md
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@@ -692,7 +692,7 @@ This performance test supports the following models:
* [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
To set up your environment and download the models using the Hugging Face CLI,
use the following commands. Modify the `huggingface-cli download` command
use the following commands. Modify the `hf download` command
to download the desired model.
```bash
@@ -704,7 +704,7 @@ pip install huggingface_hub
# Download the model to the shared NFS mount point
# Replace 'deepseek-ai/DeepSeek-R1-0528' with your desired model
huggingface-cli download --token <your_hf_token> \
hf download --token <your_hf_token> \
deepseek-ai/DeepSeek-R1-0528 \
--local-dir /mount/point/models/DeepSeek-R1
```
docs/how-to/rocm-for-ai/inference/benchmark-docker/vllm-mori-distributed.md
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@@ -387,7 +387,7 @@ source ~/venvs/hf/bin/activate
pip install huggingface_hub
# Download the model to the shared NFS mount point
huggingface-cli download --token <your_hf_token> \
hf download --token <your_hf_token> \
EmbeddedLLM/deepseek-r1-FP8-Dynamic \
--local-dir /mount/point/models/EmbeddedLLM/deepseek-r1-FP8-Dynamic
```
docs/how-to/rocm-for-ai/inference/xdit-diffusion-inference.rst
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@@ -180,7 +180,7 @@ You can either use an existing Hugging Face cache or download the model fresh in
.. code-block:: shell
huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
hf download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
3. Launch the container with mounted cache.
@@ -237,7 +237,7 @@ You can either use an existing Hugging Face cache or download the model fresh in
.. code-block:: shell
export HF_HOME=/app/huggingface_models
huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
hf download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
.. warning::
docs/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
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@@ -631,8 +631,8 @@ To launch the training job on a SLURM cluster for Llama 3.3 70B, run the followi
.. code-block:: shell
huggingface-cli login # Get access to HF Llama model space
huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
hf auth login # Get access to HF Llama model space
hf download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
# In the MAD repository
cd scripts/pytorch_train
sbatch Torchtune_Multinode.sh