[Tool Parser]Add tool parser for NVIDIA-Nemotron-Nano-9B-v2 model

[sniper35]

Purpose

Closes #42065
Nemotron cookbook updated to align with vllm codebase: NVIDIA-NeMo/Nemotron#196
Related: HF repo update: https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2/discussions/37

Test Plan

start the server with the new registered tool parser:

  export VLLM_VENV_BIN=${VLLM_VENV_BIN:-/dev-env/repos/vllm/.venv/bin}
  export MODEL=${MODEL:-/dev-env/repos/NVIDIA-Nemotron-Nano-9B-v2}
  export PORT=${PORT:-8017}

  "$VLLM_VENV_BIN/python" -m vllm.entrypoints.openai.api_server \
    --model "$MODEL" \
    --served-model-name nemotron-smoke \
    --trust-remote-code \
    --dtype bfloat16 \
    --mamba-ssm-cache-dtype float32 \
    --enable-auto-tool-choice \
    --tool-call-parser nemotron_nano_v2 \
    --chat-template examples/tool_chat_template_nemotron_nano_v2.jinja \
    --max-model-len 2048 \
    --gpu-memory-utilization 0.75 \
    --host 127.0.0.1 \
    --port "$PORT" \
    --no-enable-log-requests

Non-streming:

  from openai import OpenAI

  client = OpenAI(base_url=f"http://127.0.0.1:{__import__('os').environ.get('PORT', '8017')}/v1", api_key="dummy")

  resp = client.chat.completions.create(
      model="nemotron-smoke",
      messages=[{"role": "user", "content": "What is an 18% tip on a $100 bill? Use the tool."}],
      tools=[{
          "type": "function",
          "function": {
              "name": "calculate_tip",
              "description": "Calculate a tip amount.",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "bill_total": {"type": "number"},
                      "tip_percentage": {"type": "number"},
                  },
                  "required": ["bill_total", "tip_percentage"],
              },
          },
      }],
      tool_choice="auto",
      temperature=0,
  )

  msg = resp.choices[0].message
  print("content:", msg.content)
  print("tool_calls:", msg.tool_calls)
  assert msg.tool_calls
  assert msg.tool_calls[0].function.name == "calculate_tip"

Streaming:

  from openai import OpenAI
  import os

  client = OpenAI(base_url=f"http://127.0.0.1:{os.environ.get('PORT', '8017')}/v1", api_key="dummy")

  chunks = client.chat.completions.create(
      model="nemotron-smoke",
      messages=[{"role": "user", "content": "What is an 18% tip on a $100 bill? Use the tool."}],
      tools=[{
          "type": "function",
          "function": {
              "name": "calculate_tip",
              "description": "Calculate a tip amount.",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "bill_total": {"type": "number"},
                      "tip_percentage": {"type": "number"},
                  },
                  "required": ["bill_total", "tip_percentage"],
              },
          },
      }],
      tool_choice="auto",
      temperature=0,
      stream=True,
  )

  content = []
  tool_calls = {}

  for chunk in chunks:
      delta = chunk.choices[0].delta
      if delta.content:
          content.append(delta.content)
      for tc in delta.tool_calls or []:
          state = tool_calls.setdefault(tc.index, {"name": "", "arguments": ""})
          if tc.function and tc.function.name:
              state["name"] += tc.function.name
          if tc.function and tc.function.arguments:
              state["arguments"] += tc.function.arguments

  print("content:", "".join(content))
  print("tool_calls:", tool_calls)
  assert tool_calls
  assert any(tc["name"] == "calculate_tip" for tc in tool_calls.values())

Test Result

Non-streming:

content: Okay, the user is asking for an 18% tip on a $100 bill and wants me to use the tool. Let me check the available functions. There's a calculate_tip function that takes bill_total and tip_percentage. The parameters are both required. The bill here is $100, and the tip percentage is 18. I need to call the function with these values. Let me make sure the parameters are numbers. Yes, 100 and 18 are numbers. So the tool call should be calculate_tip with bill_total 100 and tip_percentage 18. That should give the tip amount. I don't see any missing info here, so I can proceed to call the tool.
</think>


tool_calls: [ChatCompletionMessageFunctionToolCall(id='chatcmpl-tool-bc97c3bce8f6591e', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]

Streaming:

content: Okay, the user is asking for an 18% tip on a $100 bill and wants me to use the tool. Let me check the available functions. There's a calculate_tip function that takes bill_total and tip_percentage. The parameters are both required. The bill here is $100, and the tip percentage is 18. I need to call the function with these values. Let me make sure the parameters are numbers. Yes, 100 and 18 are numbers. So the tool call should be calculate_tip with bill_total 100 and tip_percentage 18. That should give the tip amount. I don't see any missing info here, so I can proceed to call the tool.
</think>


tool_calls: {0: {'name': 'calculate_tip', 'arguments': '{"bill_total": 100, "tip_percentage": 18}'}}

test_nanotron_streaming.py
test_nanotron_non_streaming.py

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[mergify]

Documentation preview: https://vllm--42255.org.readthedocs.build/en/42255/

[gemini-code-assist]

Code Review

This PR adds the NemotronJSONToolParser and a corresponding Jinja2 chat template to support tool calling for Nemotron models. It includes documentation and tests for streaming and non-streaming tool extraction. Feedback points out a potential JSON escaping issue in the Jinja2 template, recommending the tojson filter for function names instead of manual quoting.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[sniper35]

This is to align with with the latest code base

[ameyasm1154]

I am from the Nemotron team and had worked on this model. The tool-parser implementation looks correct and the tests show it functions correctly.