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VLLM Tool Calling Guide

A battle-tested guide to getting tool calling working reliably with open source models on VLLM.

This is not a model. This is a collection of production-tested configurations, prompt templates, Python examples, and hard-won lessons from building multi-step tool calling systems with open source LLMs on NVIDIA Blackwell GPUs.

Everything here was discovered through real deployment — not theory.

Quick Start

Launch VLLM with tool calling (Hermes-3 70B):

bash

python -m vllm.entrypoints.openai.api_server \
  --model NousResearch/Hermes-3-Llama-3.1-70B-FP8 \
  --dtype auto \
  --quantization compressed-tensors \
  --max-model-len 131072 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --gpu-memory-utilization 0.90 \
  --max-num-seqs 4

Test it works:

bash

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "NousResearch/Hermes-3-Llama-3.1-70B-FP8",
    "messages": [{"role": "user", "content": "What is the weather in San Francisco?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "City name"}
          },
          "required": ["location"]
        }
      }
    }],
    "tool_choice": "auto"
  }'

If you see "tool_calls" in the response, you're good. Read on for the details.

What This Repository Contains

Directory	Contents
`configs/`	Production VLLM launch scripts for 4 models with inline documentation
`examples/`	Working Python code: basic tool calls, multi-step orchestration, JSON extraction
`prompts/`	System prompt templates for tool calling (Hermes-specific and model-agnostic)
`chat_templates/`	Jinja2 chat templates for Hermes-3 tool calling
`guides/`	Deep-dive guides on specific topics (context length, prompt engineering, troubleshooting)

Model Comparison

All models tested on NVIDIA RTX 6000 Pro Blackwell (96GB VRAM), single GPU.

Model	Size	Quant	VLLM Parser	Speed	Memory	Context	Tool Quality	Open WebUI
Hermes-3-Llama-3.1-70B	70B	FP8	`hermes`	25-35 tok/s	~40GB	128K	Excellent	No
Llama-3.3-70B-Instruct	70B	FP8	`llama3_json`	60-90 tok/s	~40GB	128K	Excellent	Yes
Qwen2-72B-Instruct	72B	FP8	`hermes`	60-90 tok/s	~45GB	128K	Very Good	Yes
Mistral-Nemo-Instruct	12B	FP8	`mistral`	100-150 tok/s	~15GB	128K	Good	Yes

Recommendations:

Best overall tool calling: Hermes-3-Llama-3.1-70B (purpose-built for function calling)
Best for Open WebUI: Llama-3.3-70B-Instruct (works out of the box)
Best speed/quality ratio: Mistral-Nemo-12B (fast iterations, good enough for most tasks)
Best multilingual: Qwen2-72B (strong across languages)

See guides/MODEL_COMPARISON.md for the full breakdown.

The Critical Context Length Fix

This is the #1 issue people hit with VLLM tool calling.

VLLM defaults to short context windows. Tool calling needs much more:

System prompt:        3-5K tokens
Tool definitions:     2-4K tokens per tool
Conversation history: 2-10K tokens
Tool responses:       5-20K tokens
─────────────────────────────────
Total needed:         20-40K+ tokens

If your context window is 16K (the default for many configs), tool calls get silently truncated mid-generation.

The fix:

bash

# BEFORE (broken): Default or small context
--max-model-len 16384

# AFTER (working): Full context support
--max-model-len 131072          # 128K tokens
--max-num-seqs 4                # Reduce concurrency to fit KV cache
--max-num-batched-tokens 132000 # Match context length
--gpu-memory-utilization 0.90   # Leave headroom

Memory math for 96GB GPU:

Model weights (FP8 70B): ~40GB
KV cache for 128K context: ~45-50GB
Total: fits with batch size 4

See guides/CONTEXT_LENGTH_FIX.md for the full analysis.

Tool Call Formats

VLLM supports multiple tool call formats. Which one you use depends on your model:

Hermes Format (ChatML + XML tags)

<|im_start|>assistant
<tool_call>
{"name": "get_weather", "arguments": {"location": "San Francisco"}}
</tool_call>
<|im_end|>

Parser flag: --tool-call-parser hermes Models: Hermes-3, Hermes-2-Pro, Qwen2

Llama 3 JSON Format

{"name": "get_weather", "parameters": {"location": "San Francisco"}}

Parser flag: --tool-call-parser llama3_json Models: Llama-3.1, Llama-3.3

Mistral Format

[TOOL_CALLS] [{"name": "get_weather", "arguments": {"location": "San Francisco"}}]

Parser flag: --tool-call-parser mistral Models: Mistral-Nemo, Mistral-7B

All formats are converted to OpenAI-compatible JSON by VLLM. Your application code always receives the same standardized format regardless of which parser is used.

See guides/TOOL_CALL_FORMATS.md for detailed comparison.

7 Prompt Engineering Lessons for Tool Calling

These lessons were learned through production debugging. Each one cost hours to diagnose.

1. LLMs Learn from Your Examples

Problem: LLM wraps all JSON responses in markdown code blocks (```json ... ```).

Root cause: Your prompt examples showed JSON inside markdown code blocks. The LLM learned to replicate the formatting.

Fix: Show raw JSON in all examples. Add explicit instruction: "Do NOT wrap your response in markdown code blocks."

2. Jinja2 Escaping Leaks into Output

Problem: LLM outputs {{ instead of { in JSON.

Root cause: Your Jinja2 chat template examples used {{ for escaping. The LLM learned to double braces.

Fix: Use single braces in all prompt examples. Handle template escaping separately from content.

3. Explicitly Limit Tool Call Blocks

Problem: LLM creates multiple <tool_call> blocks or nests them 5 levels deep.

Root cause: No instruction telling it not to.

Fix: Add: "Use ONLY ONE <tool_call> block per response. Do NOT create multiple blocks or nest them."

4. Track Validation Results, Not Just Calls

Problem: System checks if validation tools were called but not if they passed. LLM returns "success" with invalid output.

Fix:

python

# BAD: Only tracks if called
tracking = {'validate_called': False}

# GOOD: Tracks if called AND passed
tracking = {
    'validate_called': False,
    'validate_passed': False,  # Did it return valid: true?
    'validation_errors': []     # What went wrong?
}

5. Feed Errors Back with Structure

Problem: Validation fails but the LLM doesn't know what failed or how to fix it.

Fix: Format errors with property names, error types, and suggested fixes:

python

errors_formatted = "\n\nValidation Errors Found:\n"
for i, error in enumerate(errors, 1):
    errors_formatted += f"\n{i}. "
    if 'property' in error:
        errors_formatted += f"Property: {error['property']}\n"
    if 'message' in error:
        errors_formatted += f"   Message: {error['message']}\n"
    if 'fix' in error:
        errors_formatted += f"   Fix: {error['fix']}\n"

6. Use `raw_decode` for Robust JSON Extraction

Problem: LLM adds conversational text before/after the JSON: "Here is the result: {...} Let me know if you need anything else!"

Fix: Three-layer extraction:

python

import json
from json import JSONDecoder
import re

def extract_json(text: str):
    # Layer 1: Strip markdown code blocks
    if "```" in text:
        match = re.search(r'```(?:json)?\s*\n(.*?)\n```', text, re.DOTALL)
        if match:
            text = match.group(1).strip()

    # Layer 2: Find first { or [ (skip preamble)
    if not text.startswith(('{', '[')):
        for char in ['{', '[']:
            idx = text.find(char)
            if idx != -1:
                text = text[idx:]
                break

    # Layer 3: raw_decode stops at end of valid JSON (skip postamble)
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        decoder = JSONDecoder()
        data, _ = decoder.raw_decode(text)
        return data

7. Budget Enough Iterations for Multi-Step Workflows

Problem: Multi-step tool calling runs out of iterations before completing.

Root cause: Each step needs multiple LLM turns:

Get information (tool call)
Process results (tool call)
Validate output (tool call)
Fix errors if needed (tool call)
Return final response

Recommended iteration budgets:

Workflow Complexity	Max Iterations	Step Retry Limit
Simple (1-2 tools)	5	2
Medium (3-5 tools)	10	3
Complex (6+ tools)	15	3

See guides/PROMPT_ENGINEERING_LESSONS.md for code examples for each lesson.

Multi-Step Workflow Architecture

For complex tasks, single-prompt tool calling is unreliable. Break it into steps with isolated tool sets:

Step 1: Discovery          Step 2: Configuration
┌─────────────────┐       ┌─────────────────────┐
│ Tools:           │       │ Tools:               │
│ - search         │  ──>  │ - get_details        │
│ - list           │       │ - validate_minimal   │
│ - get_info       │       │ - validate_full      │
│                  │       │                      │
│ Output: What     │       │ Output: How          │
│ components to    │       │ to configure them    │
│ use              │       │                      │
└─────────────────┘       └─────────────────────┘

Key patterns:

Isolated tool sets per step — each step only sees relevant tools, reducing confusion
Pydantic schema validation — validate LLM responses structurally, not just syntactically
Retry with error feedback — when validation fails, feed structured errors back to the LLM
Result tracking — track whether validations passed, not just whether they were called

See guides/MULTI_STEP_WORKFLOWS.md for the full architecture. See examples/multi_step_orchestrator.py for working code.

Blackwell GPU Notes

If you're running on NVIDIA RTX 6000 Pro Blackwell (or similar Blackwell architecture):

FlashInfer Bug (SM120)

FlashInfer has known issues with Blackwell's SM120 compute architecture. Symptoms: crashes, hangs, or incorrect output.

bash

# Workaround: Disable FlashInfer, use FlashAttention-2 instead
export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export VLLM_USE_FLASHINFER=0

FP8 Quantization Types

Not all FP8 models use the same quantization method:

Model	Quantization Flag	Notes
Hermes-3-Llama-3.1-70B-FP8	`--quantization compressed-tensors`	Uses compressed-tensors format
Llama-3.3-70B-Instruct-FP8	`--quantization fp8_e4m3`	Native FP8, faster on Blackwell
Qwen2-72B-Instruct-FP8	`--quantization fp8`	Standard FP8
Mistral-Nemo-FP8	`--quantization fp8`	Standard FP8

Using the wrong flag won't crash — but you'll lose performance. compressed-tensors doesn't leverage Blackwell's native FP8 acceleration.

Troubleshooting

Tool calls get cut off mid-generation

Cause: Context window too small.

Fix: Increase --max-model-len to 131072 (128K). See Context Length Fix.

Model responds with text instead of tool calls

Cause: Missing --enable-auto-tool-choice flag, or system prompt doesn't instruct tool use.

Fix:

Add --enable-auto-tool-choice to VLLM launch
Add --tool-call-parser hermes (or appropriate parser)
Ensure tools are passed in the API request

Very slow generation (2-3 tok/s on 70B)

Cause: Wrong quantization method or FlashInfer issues on Blackwell.

Fix:

bash

export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export VLLM_USE_FLASHINFER=0

Also verify you're using the correct --quantization flag for your model.

Model hallucinates tool/function names

Cause: Tool definitions are too vague, or the model is guessing from training data.

Fix:

Include includeExamples: true in tool definitions to show real configurations
Add existence validation after tool calls (verify the tool response is valid before proceeding)
Use specific, descriptive tool names

Hermes-3 tool calls don't work in Open WebUI

Cause: Open WebUI expects OpenAI-format tool calls. Hermes-3's native format (ChatML + XML) isn't compatible.

Fix: Switch to Llama-3.3-70B-Instruct which works out of the box with Open WebUI. See guides/OPEN_WEBUI_COMPATIBILITY.md.

FlashInfer crashes on Blackwell GPU

Cause: FlashInfer has known bugs with SM120 (Blackwell) compute architecture.

Fix:

bash

export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export VLLM_USE_FLASHINFER=0

Open WebUI Compatibility

Model	Tool Calling via API	Tool Calling in Open WebUI
Hermes-3-Llama-3.1-70B	Yes	No (format incompatible)
Llama-3.3-70B-Instruct	Yes	Yes
Qwen2-72B-Instruct	Yes	Yes
Mistral-Nemo-12B	Yes	Yes

If you need Open WebUI support, use Llama 3.3 or Qwen2. If you're building a custom application that talks directly to the VLLM API, all models work.

See guides/OPEN_WEBUI_COMPATIBILITY.md for details.

Verified FP8 Models

All models listed below have been verified to exist on Hugging Face and work with VLLM for tool calling:

70B+ Models (High Performance):

NousResearch/Hermes-3-Llama-3.1-70B-FP8 — Best tool calling
nvidia/Llama-3.3-70B-Instruct-FP8 — Best Open WebUI support
RedHatAI/Qwen2-72B-Instruct-FP8 — Best multilingual

12B Models (Fast Iteration):

RedHatAI/Mistral-Nemo-Instruct-2407-FP8 — 100-150 tok/s

Memory Requirements (single GPU):

70B FP8: ~40-50GB
12B FP8: ~12-15GB

Citation

If you find this guide useful, please star the repository and share it.

bibtex

@misc{odmark2025vllmtoolcalling,
  title={VLLM Tool Calling Guide: Open Source Models on Blackwell GPUs},
  author={Joshua Eric Odmark},
  year={2025},
  url={https://huggingface.co/joshuaeric/vllm-tool-calling-guide}
}

Acknowledgments

NousResearch for Hermes-3 and pioneering open source tool calling
vLLM Project for the inference engine
NVIDIA and Red Hat AI / NeuralMagic for FP8 quantized models

License

Apache 2.0 — use freely, attribution appreciated.

joshuaeric/vllm-tool-calling-guide

VLLM Tool Calling Guide

Quick Start

What This Repository Contains

Model Comparison

The Critical Context Length Fix

Tool Call Formats

Hermes Format (ChatML + XML tags)

Llama 3 JSON Format

Mistral Format

7 Prompt Engineering Lessons for Tool Calling

1. LLMs Learn from Your Examples

2. Jinja2 Escaping Leaks into Output

3. Explicitly Limit Tool Call Blocks

4. Track Validation Results, Not Just Calls

5. Feed Errors Back with Structure

6. Use raw_decode for Robust JSON Extraction

7. Budget Enough Iterations for Multi-Step Workflows

Multi-Step Workflow Architecture

Blackwell GPU Notes

FlashInfer Bug (SM120)

FP8 Quantization Types

Troubleshooting

Open WebUI Compatibility

Verified FP8 Models

Citation

Acknowledgments

License

joshuaeric/
vllm-tool-calling-guide

6. Use `raw_decode` for Robust JSON Extraction