diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 10c51e0..736f36d 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -11,9 +11,59 @@ env:
 
 jobs:
   # Job for building on macOS (with Metal)
+  build-linux:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Python virtual environment
+        shell: bash
+        run: |
+          python3 -m venv .venv
+          source .venv/bin/activate
+          python3 -m pip install --upgrade pip
+          python3 -m pip install huggingface-hub
+      - name: Login to HuggingFace Hub
+        env:
+          HF_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
+        run: |
+          source .venv/bin/activate
+          if [ -n "$HF_TOKEN" ]; then
+            echo "Logging into Hugging Face Hub..."
+            huggingface-cli login --token $HF_TOKEN
+          else
+            echo "No HuggingFace token found, skipping login"
+          fi
+      - uses: oven-sh/setup-bun@v2
+      - run: bun install
+      # If you intend to use CUDA on Linux (highly recommended for performance with Candle)
+      # You'll need a runner with GPU support or use an action that sets up CUDA.
+      # GitHub's default ubuntu-latest runners are CPU-only.
+      # For GPU runners, you'd typically use self-hosted runners or a service like vast.ai.
+      # For CI, you might build a CPU-only version for faster checks, or use a GPU-enabled runner for full testing.
+
+      # Option 1: Build with CPU backend (no CUDA, no Metal)
+      - name: Build (Linux - CPU only)
+        run: cargo build --release --no-default-features
+
+      # Option 2: Build with CUDA backend (requires CUDA setup and often a GPU runner)
+      # - name: Setup CUDA (example - specific action might vary)
+      #   uses: jimmy-shian/setup-cuda@v1 # This is just an example, research appropriate action
+      #   with:
+      #     cuda-version: '12.0' # Or your desired CUDA version
+      # - name: Build (Linux - CUDA)
+      #   # Explicitly enable cudarc and candle-core's cuda feature, and potentially intel-mkl-src
+      #   run: cargo build --release --features "cudarc candle-core/cuda intel-mkl-src"
+
+      - name: Run tests (Linux - CPU only)
+        # If your tests are CPU-only, they'll work with the CPU build.
+        # If they require CUDA, they'd need the CUDA build and runner.
+        run: cargo test --no-default-features
+
   build-macos:
     runs-on: macos-latest
 
+
+
     steps:
       - uses: actions/checkout@v4
       - name: Setup Python virtual environment