Merge f75d5817aa into 8452a1c5b1

nvidia/nemo-fine-tune/README.md

@@ -47,8 +47,8 @@ All necessary files for the playbook can be found [here on GitHub](https://githu
 * **Duration:** 45-90 minutes for complete setup and initial model fine-tuning
 * **Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting, distributed training setup complexity increases with multi-node configurations
 * **Rollback:** Virtual environments can be completely removed; no system-level changes are made to the host system beyond package installations.
-* **Last Updated:** 01/15/2026
+* **Last Updated:** 03/04/2026
-  * Fix qLoRA fine-tuning workflow
+  * Recommend running Nemo finetune workflow via Docker
 
 ## Instructions
 

nvidia/nemoclaw/README.md

@@ -172,12 +172,15 @@ Verify the NVIDIA runtime works:
 docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
 ```
 
-If you get a permission denied error on `docker`, add your user to the Docker group and log out/in:
+If you get a permission denied error on `docker`, add your user to the Docker group and activate the new group in your current session:
 
 ```bash
 sudo usermod -aG docker $USER
+newgrp docker
 ```
 
+This applies the group change immediately. Alternatively, you can log out and back in instead of running `newgrp docker`.
+
 > [!NOTE]
 > DGX Spark uses cgroup v2. OpenShell's gateway embeds k3s inside Docker and needs host cgroup namespace access. Without `default-cgroupns-mode: host`, the gateway can fail with "Failed to start ContainerManager" errors.
 
@@ -322,13 +325,21 @@ http://127.0.0.1:18789/#token=<long-token-here>
 
 **If accessing the Web UI from a remote machine**, you need to set up port forwarding.
 
+First, find your Spark's IP address. On the Spark, run:
+
+```bash
+hostname -I | awk '{print $1}'
+```
+
+This prints the primary IP address (e.g. `192.168.1.42`). You can also find it in **Settings > Wi-Fi** or **Settings > Network** on the Spark's desktop, or check your router's connected-devices list.
+
 Start the port forward on the Spark host:
 
 ```bash
 openshell forward start 18789 my-assistant --background
 ```
 
-Then from your remote machine, create an SSH tunnel to the Spark:
+Then from your remote machine, create an SSH tunnel to the Spark (replace `<your-spark-ip>` with the IP address from above):
 
 ```bash
 ssh -L 18789:127.0.0.1:18789 <your-user>@<your-spark-ip>

nvidia/txt2kg/assets/deploy/compose/docker-compose.yml

@@ -27,8 +27,8 @@ services:
       # Ollama configuration
       - OLLAMA_BASE_URL=http://ollama:11434/v1
       - OLLAMA_MODEL=llama3.1:8b
-      # Disable vLLM
+      # vLLM disabled in default Ollama mode
-      - VLLM_BASE_URL=http://localhost:8001/v1
+      # - VLLM_BASE_URL=http://localhost:8001/v1
       - VLLM_MODEL=disabled
       # Vector DB configuration
       - QDRANT_URL=http://qdrant:6333

nvidia/txt2kg/assets/frontend/lib/text-processor.ts

@@ -108,7 +108,7 @@ export class TextProcessor {
     
     // Determine which LLM provider to use based on configuration
     // Priority: vLLM > NVIDIA > Ollama
-    if (process.env.VLLM_BASE_URL) {
+    if (process.env.VLLM_BASE_URL && process.env.VLLM_MODEL && process.env.VLLM_MODEL !== 'disabled') {
       this.selectedLLMProvider = 'vllm';
     } else if (process.env.NVIDIA_API_KEY) {
       this.selectedLLMProvider = 'nvidia';

nvidia/vllm/README.md

@@ -82,7 +82,7 @@ The following models are supported with vLLM on Spark. All listed models are ava
 | **Nemotron3-Nano** | FP8 | ✅ | [`nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8`](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8) |
 
 > [!NOTE]
-> The Phi-4-multimodal-instruct models require `--trust-remote-code` when launching vLLM.
+> The Phi-4-multimodal-instruct and Nemotron3-Nano models require `--trust-remote-code` when launching vLLM.
 
 > [!NOTE]
 > You can use the NVFP4 Quantization documentation to generate your own NVFP4-quantized checkpoints for your favorite models. This enables you to take advantage of the performance and memory benefits of NVFP4 quantization even for models not already published by NVIDIA.