docs: update README and forge documentation

- README: Fixed backend command, added GPU compatibility reference
- forge.md: Fixed train CLI (--output-dir), added GPU troubleshooting
- Added reference to GPU compatibility guide for RTX 50-series

docs/forge.md

@@ -193,22 +193,24 @@ new_path = reload_model(
 
 ```bash
 # Extract training data
-companion.forge.cli extract [--output PATH]
+python -m companion.forge.cli extract [--output PATH]
 
 # Train model
-companion.forge.cli train \
-  [--data PATH] \
-  [--output PATH] \
-  [--epochs N] \
-  [--lr FLOAT]
+python -m companion.forge.train \
+  --data PATH \
+  --output-dir PATH \
+  --epochs N \
+  --lr FLOAT
 
 # Check model status
-companion.forge.cli status
+python -m companion.forge.cli status
 
 # Reload model
-companion.forge.cli reload MODEL_PATH [--no-backup]
+python -m companion.forge.cli reload MODEL_PATH [--no-backup]
 ```
 
+**Note:** Use `--output-dir` (or `--output`) to specify the training output directory.
+
 ## Training Tips
 
 **Dataset Size:**
@@ -243,6 +245,11 @@ Use `gradient_accumulation_steps` to increase effective batch size.
 
 ## Troubleshooting
 
+**GPU Not Detected / CUDA Not Available**
+- See [GPU Compatibility Guide](gpu-compatibility.md)
+- Common issue on RTX 50-series: Install CUDA-enabled PyTorch: `pip install torch==2.5.1+cu121 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121`
+- Verify: `python -c "import torch; print(torch.cuda.is_available())"`
+
 **CUDA Out of Memory**
 - Reduce `lora_rank` to 8
 - Reduce `batch_size` to 2