Added Nemotron Coder-R demo showing the capabilities of NVIDIA Nemotron Nano 9B v2

community/README.md

@@ -86,4 +86,8 @@ Community examples are sample code and deployments for RAG pipelines that are no
 
 * [Chat with LLM Llama 3.1 Nemotron Nano 4B](./chat-llama-nemotron/)
 
-  This is a React-based conversational UI designed for interacting with a powerful local LLM. It incorporates RAG to enhance contextual understanding and is backed by an NVIDIA Dynamo inference server running the NVIDIA Llama-3.1-Nemotron-Nano-4B-v1.1 model. The setup enables low-latency, cloud-free AI assistant capabilities, with live document search and reasoning, all deployable on local or edge infrastructure.
+  This is a React-based conversational UI designed for interacting with a powerful local LLM. It incorporates RAG to enhance contextual understanding and is backed by an NVIDIA Dynamo inference server running the NVIDIA Llama-3.1-Nemotron-Nano-4B-v1.1 model. The setup enables low-latency, cloud-free AI assistant capabilities, with live document search and reasoning, all deployable on local or edge infrastructure.
+
+* [Reasoning Coder](./reasoning_coder/)
+
+  Reasoning Coder is a demonstration of a coding agent for reasoning-aware code generation powered by the open-source [NVIDIA Nemotron Nano 9B v2 model](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2). The agent combines the strengths of large language model coding capabilities with a reasoning budget control mechanism, enabling more transparent and efficient problem-solving. It is designed to showcase how developers can integrate self-hosted vLLM deployments to run advanced code assistants locally or on their own infrastructure. The demo highlights how NVIDIA Nemotron Nano 9B v2 reasoning features can be applied to software development workflows, making it easier to experiment with streaming, non-streaming, and reasoning-driven code generation in a reproducible environment.

community/reasoning_coder/.gitignore

@@ -0,0 +1,56 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+venv/
+env/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Streamlit
+.streamlit/
+
+# Logs
+*.log
+
+# Environment variables
+.env
+.env.local
+.env.*.local 

community/reasoning_coder/README.md

@@ -0,0 +1,104 @@
+# Reasoning Coder
+
+Reasoning Coder is a demonstration of a coding agen for reasoning-aware code generation powered by the open-source [NVIDIA Nemotron Nano 9B v2 model](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2). The agent combines the strengths of large language model coding capabilities with a reasoning budget control mechanism, enabling more transparent and efficient problem-solving. 
+
+It is designed to showcase how developers can integrate self-hosted vLLM deployments to run advanced code assistants locally or on their own infrastructure. The demo highlights how NVIDIA Nemotron Nano 9B v2 reasoning features can be applied to software development workflows, making it easier to experiment with streaming, non-streaming, and reasoning-driven code generation in a reproducible environment.
+
+## Features
+
+- **Reasoning Budget Control**: Toggle reasoning on/off and control token budget
+- **Streaming Support**: Real-time streaming of responses
+- **Code Generation**: AI-powered code generation for various programming languages
+- **File Upload Context**: Upload files to provide context for better code generation
+
+## Requirements
+
+- **vLLM server** running with Nemotron Nano 9B v2 model
+- **Hugging Face token** to download the model. Get one [here](https://huggingface.co/settings/tokens).
+- **Python 3.8+** environment
+- **Docker** (optional)
+
+## vLLM Server Setup
+
+### Basic vLLM Installation
+```bash
+pip install -U "vllm>=0.10.1"
+```
+
+Alternativly, you can use Docker to launch a vLLM server. See the instructions below.
+
+## Quick Start
+
+### 1. Start your vLLM server
+
+```bash
+vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
+    --trust-remote-code \
+    --mamba_ssm_cache_dtype float32 \
+    --max-num-seqs 64 \
+    --max-model-len 131072 \
+    --host 0.0.0.0 \
+    --port 8888
+```
+
+Or, if you are using Docker:
+
+```bash
+export HF_CACHE_DIR=<your_local_HF_directory>
+export HF_TOKEN=<your_HF_token>
+export TP_SIZE=1
+
+docker run --runtime nvidia --gpus all --ipc=host \
+  -v "$HF_CACHE_DIR":/hf_cache \
+  -e HF_HOME=/hf_cache \
+  -e "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+  -e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+  -p 8888:8888 \
+  vllm/vllm-openai:v0.10.1 \
+    --model nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
+    --tensor-parallel-size ${TP_SIZE} \
+    --trust-remote-code \
+    --mamba_ssm_cache_dtype float32 \
+    --max-num-seqs 64 \
+    --max-model-len 131072 \
+    --host 0.0.0.0 \
+    --port 8888
+```
+
+#### Customize Endpoint
+If you're running vLLM on a different port or host, update the `DEFAULT_LOCAL_API` constant in `reasoning_coder.py`.
+
+
+### 2. Setup the Coding Client
+
+Clone this repository
+
+```bash
+git clone https://github.com/NVIDIA/GenerativeAIExamples.git
+cd GenerativeAIExamples/community/reasoning-coder
+```
+
+Activate virtual environment and install dependencies.
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+pip install -r requirements.txt
+```
+
+### 3. Run Demo
+```bash
+streamlit run reasoning_coder.py
+```
+
+The UI should open in the browser under http://localhost:8501/.
+
+## Example Prompts
+
+Try these built-in examples:
+- "Write a Python function to find the longest palindromic substring in a string"
+- "Create a recursive function to solve the Tower of Hanoi puzzle"
+- "Implement a binary search tree with insertion and search operations"
+- "Write a function to validate email addresses using regex"
+- "Create a simple web scraper using Python requests and BeautifulSoup"
+