docs: resolve leftover lora_llama3_demo.ipynb comments and relative code links in lora_model_bringup.md

Author: RexBearIU

docs/guides/lora_model_bringup.md

@@ -30,12 +30,12 @@ To enable LoRA support for a new model, follow these two simple steps:
 
 The target model architecture must already be implemented and supported as a base model in MaxText.
 
-- The JAX/NNX model definition should be located under `src/maxtext/models/` (e.g., \[gemma3.py\](../../src/maxtext/models/gemma3.py)).
+- The JAX/NNX model definition should be located under `src/maxtext/models/` (e.g., [gemma3.py](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/models/gemma3.py)).
 - The model configurations must be registered and runnable for baseline pre-training or full fine-tuning.
 
 ### Step 1.2: Define Trainable LoRA Target Modules
 
-Add a recommended target pattern for your model architecture prefix in \[src/maxtext/configs/post_train/lora_module_path.yml\](../../src/maxtext/configs/post_train/lora_module_path.yml):
+Add a recommended target pattern for your model architecture prefix in [src/maxtext/configs/post_train/lora_module_path.yml](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/configs/post_train/lora_module_path.yml):
 
 ```yaml
 your_model_prefix: "decoder/layers/.*(self_attention/(query|key|value|out)|mlp/(wi_0|wi_1|wo))"
@@ -69,7 +69,7 @@ If you want to perform decoding or run high-performance serving on your adapted
 To add weight mapping for vLLM decode:
 
 1. **Create a Weight Mapping Config**:
-   Create a new file in \[src/maxtext/integration/tunix/weight_mapping/\](../../src/maxtext/integration/tunix/weight_mapping/) (e.g., `your_model.py`) defining a mapping dataclass. You can refer to \[gemma3.py\](../../src/maxtext/integration/tunix/weight_mapping/gemma3.py) or \[llama3.py\](../../src/maxtext/integration/tunix/weight_mapping/llama3.py) as templates.
+   Create a new file in [src/maxtext/integration/tunix/weight_mapping/](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/integration/tunix/weight_mapping/) (e.g., `your_model.py`) defining a mapping dataclass. You can refer to [llama3.py](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/integration/tunix/weight_mapping/llama3.py) as a template.
 
    Your class should specify:
 
@@ -78,7 +78,7 @@ To add weight mapping for vLLM decode:
    - `lora_to_hf_mappings()`: Custom mapping for LoRA weights if they require different handling.
 
 2. **Register the Mapping**:
-   Register your new class in \[src/maxtext/integration/tunix/weight_mapping/__init__.py\](../../src/maxtext/integration/tunix/weight_mapping/__init__.py) inside the `StandaloneVllmWeightMapping` class:
+   Register your new class in [src/maxtext/integration/tunix/weight_mapping/__init__.py](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/integration/tunix/weight_mapping/__init__.py) inside the `StandaloneVllmWeightMapping` class:
 
    ```python
    # Inside StandaloneVllmWeightMapping

src/maxtext/examples/lora_llama3_demo.ipynb

@@ -79,6 +79,10 @@
     "    # Install uv, a fast Python package installer\n",
     "    !pip install uv\n",
     "    \n",
+    "    # Set the torch backend to CPU for uv\n",
+    "    import os\n",
+    "    os.environ[\"UV_TORCH_BACKEND\"] = \"cpu\"\n",
+    "    \n",
     "    # Install MaxText and post-training dependencies\n",
     "    !uv pip install -e .[tpu-post-train] --resolution=lowest\n",
     "    !install_tpu_post_train_extra_deps"
@@ -215,16 +219,6 @@
    "outputs": [],
    "source": [
     "if not epath.Path(MODEL_CHECKPOINT_PATH).exists():\n",
-    "    # Install torch for the conversion script\n",
-    "    print(\"Installing torch...\")\n",
-    "    subprocess.run(\n",
-    "        [\n",
-    "            sys.executable, \"-m\", \"pip\", \"install\",\n",
-    "            \"torch\", \"--index-url\", \"https://download.pytorch.org/whl/cpu\"\n",
-    "        ],\n",
-    "        check=True\n",
-    "    )\n",
-    "\n",
     "    print(\"Converting checkpoint from HuggingFace...\")\n",
     "    env = os.environ.copy()\n",
     "    env[\"JAX_PLATFORMS\"] = \"cpu\"\n",
@@ -607,4 +601,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}