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---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- llama-3
- pytorch
base_model:
- nvidia/Llama-3.1-Minitron-4B-Width-Base
datasets:
- nvidia/Llama-Nemotron-Post-Training-Dataset
---
# Llama-3.1-Nemotron-Nano-4B-v1.1
## Model Overview
![Accuracy Comparison Plot](./accuracy_plot.png)
Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) which is a derivative of [nvidia/Llama-3.1-Minitron-4B-Width-Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base), which is created from Llama 3.1 8B using [our LLM compression technique](https://arxiv.org/abs/2408.11796) and offers improvements in model accuracy and efficiency. It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling.
Llama-3.1-Nemotron-Nano-4B-v1.1 is a model which offers a great tradeoff between model accuracy and efficiency. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K.
This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and RPO checkpoints
This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:
- [Llama-3.3-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)
- [Llama-3.3-Nemotron-Super-49B-v1](https://huggingface.co/nvidia/Llama-3.3-Nemotron-Super-49B-v1)
- [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1)
This model is ready for commercial use.
## Feature Voting
We want to hear from you! Share your ideas, vote on what matters, and help [shape the future of Nemotron](https://nemotron.ideas.nvidia.com/).
## License/Terms of Use
GOVERNING TERMS: Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.
**Model Developer:** NVIDIA
**Model Dates:** Trained between August 2024 and May 2025
**Data Freshness:** The pretraining data has a cutoff of June 2023.
## Use Case:
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Balance of model accuracy and compute efficiency (the model fits on a single RTX GPU and can be used locally).
## Release Date: <br>
5/20/2025 <br>
## References
- [\[2408.11796\] LLM Pruning and Distillation in Practice: The Minitron Approach](https://arxiv.org/abs/2408.11796)
- [\[2502.00203\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)
- [\[2505.00949\] Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)
## Model Architecture
**Architecture Type:** Dense decoder-only Transformer model
**Network Architecture:** Llama 3.1 Minitron Width 4B Base
## Intended use
Llama-3.1-Nemotron-Nano-4B-v1.1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.
# Input:
- **Input Type:** Text
- **Input Format:** String
- **Input Parameters:** One-Dimensional (1D)
- **Other Properties Related to Input:** Context length up to 131,072 tokens
## Output:
- **Output Type:** Text
- **Output Format:** String
- **Output Parameters:** One-Dimensional (1D)
- **Other Properties Related to Output:** Context length up to 131,072 tokens
## Model Version:
1.1 (5/20/2025)
## Software Integration
- **Runtime Engine:** NeMo 24.12 <br>
- **Recommended Hardware Microarchitecture Compatibility:**
- NVIDIA Hopper
- NVIDIA Ampere
## Quick Start and Usage Recommendations:
1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt
2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode
3. We recommend using greedy decoding for Reasoning OFF mode
4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required
See the snippet below for usage with Hugging Face Transformers library. Reasoning mode (ON/OFF) is controlled via system prompt. Please see the example below.
Our code requires the transformers package version to be `4.44.2` or higher.
### Example of “Reasoning On:”
```python
import torch
import transformers
model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
max_new_tokens=32768,
temperature=0.6,
top_p=0.95,
**model_kwargs
)
# Thinking can be "on" or "off"
thinking = "on"
print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}]))
```
### Example of “Reasoning Off:”
```python
import torch
import transformers
model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
max_new_tokens=32768,
do_sample=False,
**model_kwargs
)
# Thinking can be "on" or "off"
thinking = "off"
print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}]))
```
For some prompts, even though thinking is disabled, the model emergently prefers to think before responding. But if desired, the users can prevent it by pre-filling the assistant response.
```python
import torch
import transformers
model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id
# Thinking can be "on" or "off"
thinking = "off"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
max_new_tokens=32768,
do_sample=False,
**model_kwargs
)
print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}, {"role":"assistant", "content":"<think>\n</think>"}]))
```
## Running a vLLM Server with Tool-call Support
Llama-3.1-Nemotron-Nano-4B-v1.1 supports tool calling. This HF repo hosts a tool-callilng parser as well as a chat template in Jinja, which can be used to launch a vLLM server.
Here is a shell script example to launch a vLLM server with tool-call support. `vllm/vllm-openai:v0.6.6` or newer should support the model.
```shell
#!/bin/bash
CWD=$(pwd)
PORT=5000
git clone https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
docker run -it --rm \
--runtime=nvidia \
--gpus all \
--shm-size=16GB \
-p ${PORT}:${PORT} \
-v ${CWD}:${CWD} \
vllm/vllm-openai:v0.6.6 \
--model $CWD/Llama-3.1-Nemotron-Nano-4B-v1.1 \
--trust-remote-code \
--seed 1 \
--host "0.0.0.0" \
--port $PORT \
--served-model-name "Llama-Nemotron-Nano-4B-v1.1" \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--gpu-memory-utilization 0.95 \
--enforce-eager \
--enable-auto-tool-choice \
--tool-parser-plugin "${CWD}/Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_toolcall_parser.py" \
--tool-call-parser "llama_nemotron_json" \
--chat-template "${CWD}/Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_generic_tool_calling.jinja"
```
Alternatively, you can use a virtual environment to launch a vLLM server like below.
```console
$ git clone https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
$ conda create -n vllm python=3.12 -y
$ conda activate vllm
$ python -m vllm.entrypoints.openai.api_server \
--model Llama-3.1-Nemotron-Nano-4B-v1.1 \
--trust-remote-code \
--seed 1 \
--host "0.0.0.0" \
--port 5000 \
--served-model-name "Llama-Nemotron-Nano-4B-v1.1" \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--gpu-memory-utilization 0.95 \
--enforce-eager \
--enable-auto-tool-choice \
--tool-parser-plugin "Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_toolcall_parser.py" \
--tool-call-parser "llama_nemotron_json" \
--chat-template "Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_generic_tool_calling.jinja"
```
After launching a vLLM server, you can call the server with tool-call support using a Python script like below.
```python
>>> from openai import OpenAI
>>> client = OpenAI(
base_url="http://0.0.0.0:5000/v1",
api_key="dummy",
)
>>> completion = client.chat.completions.create(
model="Llama-Nemotron-Nano-4B-v1.1",
messages=[
{"role": "system", "content": "detailed thinking on"},
{"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"},
],
tools=[
{"type": "function", "function": {"name": "calculate_tip", "parameters": {"type": "object", "properties": {"bill_total": {"type": "integer", "description": "The total amount of the bill"}, "tip_percentage": {"type": "integer", "description": "The percentage of tip to be applied"}}, "required": ["bill_total", "tip_percentage"]}}},
{"type": "function", "function": {"name": "convert_currency", "parameters": {"type": "object", "properties": {"amount": {"type": "integer", "description": "The amount to be converted"}, "from_currency": {"type": "string", "description": "The currency code to convert from"}, "to_currency": {"type": "string", "description": "The currency code to convert to"}}, "required": ["from_currency", "amount", "to_currency"]}}},
],
)
>>> completion.choices[0].message.content
'<think>\nOkay, let\'s see. The user has a bill of $100 and wants to know the amount of a 18% tip. So, I need to calculate the tip amount. The available tools include calculate_tip, which requires bill_total and tip_percentage. The parameters are both integers. The bill_total is 100, and the tip percentage is 18. So, the function should multiply 100 by 18% and return 18.0. But wait, maybe the user wants the total including the tip? The question says "the amount for 18% tip," which could be interpreted as the tip amount itself. Since the function is called calculate_tip, it\'s likely that it\'s designed to compute the tip, not the total. So, using calculate_tip with bill_total=100 and tip_percentage=18 should give the correct result. The other function, convert_currency, isn\'t relevant here. So, I should call calculate_tip with those values.\n</think>\n\n'
>>> completion.choices[0].message.tool_calls
[ChatCompletionMessageToolCall(id='chatcmpl-tool-2972d86817344edc9c1e0f9cd398e999', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]
```
## Inference:
**Engine:** Transformers
**Test Hardware:**
- BF16:
- 1x RTX 50 Series GPUs
- 1x RTX 40 Series GPUs
- 1x RTX 30 Series GPUs
- 1x H100-80GB GPU
- 1x A100-80GB GPU
- Jetson AGX Thor
**Preferred/Supported] Operating System(s):** Linux <br>
## Training Datasets
A large variety of training data was used for the post-training pipeline, including manually annotated data and synthetic data.
The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.
Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both Reasoning On and Off modes, to train the model to distinguish between two modes.
**Data Collection for Training Datasets:** <br>
* Hybrid: Automated, Human, Synthetic <br>
**Data Labeling for Training Datasets:** <br>
* N/A <br>
## Evaluation Datasets
We used the datasets listed below to evaluate Llama-3.1-Nemotron-Nano-4B-v1.1.
**Data Collection for Evaluation Datasets:** Hybrid: Human/Synthetic
**Data Labeling for Evaluation Datasets:** Hybrid: Human/Synthetic/Automatic
## Evaluation Results
These results contain both “Reasoning On”, and “Reasoning Off”. We recommend using temperature=`0.6`, top_p=`0.95` for “Reasoning On” mode, and greedy decoding for “Reasoning Off” mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.
> NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below.
### MT-Bench
| Reasoning Mode | Score |
|--------------|------------|
| Reasoning Off | 7.4 |
| Reasoning On | 8.0 |
### MATH500
| Reasoning Mode | pass@1 |
|--------------|------------|
| Reasoning Off | 71.8% |
| Reasoning On | 96.2% |
User Prompt Template:
```
"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}"
```
### AIME25
| Reasoning Mode | pass@1 |
|--------------|------------|
| Reasoning Off | 13.3% |
| Reasoning On | 46.3% |
User Prompt Template:
```
"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}"
```
### GPQA-D
| Reasoning Mode | pass@1 |
|--------------|------------|
| Reasoning Off | 33.8% |
| Reasoning On | 55.1% |
User Prompt Template:
```
"What is the correct answer to this question: {question}\nChoices:\nA. {option_A}\nB. {option_B}\nC. {option_C}\nD. {option_D}\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \boxed{}"
```
### IFEval
| Reasoning Mode | Strict:Prompt | Strict:Instruction |
|--------------|------------|------------|
| Reasoning Off | 70.1% | 78.5% |
| Reasoning On | 75.5% | 82.6% |
### BFCL v2 Live
| Reasoning Mode | Score |
|--------------|------------|
| Reasoning Off | 63.6% |
| Reasoning On | 67.9% |
User Prompt Template:
```
<AVAILABLE_TOOLS>{functions}</AVAILABLE_TOOLS>
{user_prompt}
```
### MBPP 0-shot
| Reasoning Mode | pass@1 |
|--------------|------------|
| Reasoning Off | 61.9% |
| Reasoning On | 85.8% |
User Prompt Template:
````
You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
Here is the given problem and test examples:
{prompt}
Please use the python programming language to solve this problem.
Please make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.
Please return all completed codes in one code block.
This code block should be in the following format:
```python
# Your codes here
```
````
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety.md), and [Privacy](privacy.md) Subcards.
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

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|Field:|Response:|
|:---|:---|
|Participation considerations from adversely impacted groups (protected classes) in model design and testing:|None|
|Measures taken to mitigate against unwanted bias:|None|

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{
"_name_or_path": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": 128001,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 9216,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 4.0,
"low_freq_factor": 1.0,
"high_freq_factor": 4.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 3565775107.2609234,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.47.1",
"use_cache": true,
"vocab_size": 128256
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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|Field:|Response:|
|:---|:---|
|Intended Application(s) & Domain(s):|Text generation, reasoning, summarization, and question answering.|
|Model Type: |Text-to-text transformer |
|Intended Users:|This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.|
|Output:|Text String(s)|
|Describe how the model works:|Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers.|
|Technical Limitations:|The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.<br><br>The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs.<br><br>The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text.|
|Verified to have met prescribed quality standards?|Yes|
|Performance Metrics:|Accuracy, Throughput, and user-side throughput|
|Potential Known Risks:|The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.<br><br>The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.|
|End User License Agreement:|Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.|

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{
"_from_model_config": true,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"transformers_version": "4.47.1"
}

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{%- if tools %}
{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n' -}}
{%- if messages[0].role == 'system' and messages[0].content != '' -%}
{{- messages[0].content + '\n\n' -}}
{%- else -%}
{{- 'detailed thinking off\n\n' -}}
{%- endif -%}
{{- '<AVAILABLE_TOOLS>[' -}}
{%- for tool in tools -%}
{{- (tool.function if tool.function is defined else tool) | tojson -}}{{- ', ' if not loop.last else '' -}}
{%- endfor -%}
{{- ']</AVAILABLE_TOOLS>' -}}{{- '<|eot_id|>' -}}
{%- else %}
{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n' -}}
{%- if messages[0].role == 'system' and messages[0].content != '' -%}
{{- messages[0].content -}}
{%- else -%}
{{- 'detailed thinking off' -}}
{%- endif %}
{{- '<|eot_id|>' -}}
{%- endif %}
{%- for message in messages -%}
{%- if (message.role == 'user') -%}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' + message.content + '<|eot_id|>' -}}
{%- elif message.role == 'assistant' -%}
{%- set content = message.content -%}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' + content -}}
{%- if message.tool_calls -%}
{{- '<TOOLCALL>[' -}}
{%- for tool_call in message.tool_calls -%}
{%- if tool_call.function -%}
{%- set tool_call = tool_call.function -%}
{%- endif -%}
{{- '{"name": "' }}{{- tool_call.name }}{{- '", "arguments": ' -}}
{%- if tool_call.arguments is string -%}
{{- tool_call.arguments -}}
{%- else -%}
{{- tool_call.arguments | tojson -}}
{%- endif -%}
{{- ', ' if not loop.last else '' -}}
{%- endfor -%}
{{- ']</TOOLCALL>' -}}
{%- endif %}
{{- '<|eot_id|>' -}}
{%- elif message.role == 'tool' -%}
{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') -%}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' }}{{- '<TOOL_RESPONSE>[' -}}
{%- endif -%}
{{- message.content -}}{{- ', ' if not loop.last and (messages[loop.index0 + 1].role == 'tool') else '' -}}
{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') -%}
{{- ']</TOOL_RESPONSE>' -}}{{- '<|eot_id|>' -}}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{%- endif %}

109
llama_nemotron_nano_toolcall_parser.py Normal file
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# SPDX-License-Identifier: Apache-2.0
import json
import re
from collections.abc import Sequence
from typing import Union
import partial_json_parser
from partial_json_parser.core.options import Allow
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaFunctionCall, DeltaMessage,
DeltaToolCall,
ExtractedToolCallInformation,
FunctionCall,
ToolCall,
)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser,
ToolParserManager,
)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import random_uuid
logger = init_logger(__name__)
@ToolParserManager.register_module("llama_nemotron_json")
class LlamaNemotronJSONToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.current_tool_name_sent: bool = False
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.streamed_args_for_tool: list[str] = []
self.tool_call_start_token: str = "<TOOLCALL>"
self.tool_call_end_token: str = "</TOOLCALL>"
self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
if self.tool_call_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False,
tool_calls=[],
content=model_output,
)
else:
try:
str_tool_calls = self.tool_call_regex.findall(model_output)[0].strip()
if not str_tool_calls.startswith("["):
str_tool_calls = "[" + str_tool_calls
if not str_tool_calls.endswith("]"):
str_tool_calls = "]" + str_tool_calls
json_tool_calls = json.loads(str_tool_calls)
tool_calls = []
for tool_call in json_tool_calls:
try:
tool_calls.append(ToolCall(
type="function",
function=FunctionCall(
name=tool_call["name"],
arguments=json.dumps(tool_call["arguments"], ensure_ascii=False) \
if isinstance(tool_call["arguments"], dict) else tool_call["arguments"],
),
))
except:
continue
content = model_output[:model_output.rfind(self.tool_call_start_token)]
return ExtractedToolCallInformation(
tools_called=True,
tool_calls=tool_calls,
content=content if content else None,
)
except Exception:
logger.exception(f"Error in extracting tool call from response. Response: {model_output}")
return ExtractedToolCallInformation(
tools_called=False,
tool_calls=[],
content=model_output,
)
def extract_tool_calls_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
request: ChatCompletionRequest,
) -> Union[DeltaMessage, None]:
raise NotImplementedError("Tool calling is not supported in streaming mode!")

3
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|Field:|Response:|
|:---|:---|
|Generatable or Reverse engineerable personally-identifiable information?|None|
|Was consent obtained for any personal data used?|None Known|
|Personal data used to create this model?|None Known|
|How often is dataset reviewed?|Before Release|
|Is there provenance for all datasets used in training?|Yes|
|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|
|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|

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|Field:|Response:|
|:---|:---|
|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|
|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|
|Use Case Restrictions:|Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.|
|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog.|

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{
"bos_token": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|eot_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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