Pulsar 16B

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Optimized for Fast and Efficient Inference · Reduced Memory Footprint

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Table of Contents

• Model Overview
• Key Characteristics
• Quick Start
• Reasoning Control
• Tool Calling
• Training & Fine-Tuning
• Evaluation & Benchmarks
• Languages
• Safety & Limitations
• Model Information
• Citation

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Model Overview

Pulsar 16B is a model based on NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, developed by Multiverse Computing. The original model is a ~31.6B parameter, part of the Nemotron model family. It supports long-context inference up to 1M tokens and is designed for general-purpose language modeling tasks.

This version applies model compression techniques to significantly reduce parameter count and deployment requirements while maintaining compatibility with the Nemotron Hybrid Mamba2-Transformer with MoE architecture. The resulting model achieves 50% compression, reducing the parameter count to 16.15B parameters and lowering memory requirements.

• BF16
• FP8
• NVFP4

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Key Characteristics

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Quick Start

This model can be loaded with the Transformers API. Use trust_remote_code=True. Recommended approach: AutoModelForCausalLM with apply_chat_template. This configuration has been tested with Transformers 4.57.6.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "MultiverseComputingCAI/Pulsar-16B-BF16"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda" if torch.cuda.is_available() else "auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
messages = [
    {"role": "user", "content": "Write a haiku about GPUs"},
]

tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    tokenized_chat,
    max_new_tokens=1024,
    temperature=1.0,
    top_p=1.0,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))

Alternatively you can use the pipeline API with trust_remote_code=True; the pipeline returns the full conversation structure, so extract the assistant message from outputs[0]["generated_text"] as needed.

vLLM Serving

Installation

pip install -U "vllm>=0.12.0"

Reasoning parser (NVIDIA)

Pulsar 16B uses the same Nemotron v3 reasoning tags as the base model. NVIDIA provides the vLLM plugin as nano_v3_reasoning_parser.py on the base Hugging Face repo (not specific to Pulsar). Direct download:

wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py

You can keep any local filename; the vllm serve flags below assume the file is in the current directory as nano_v3_reasoning_parser.py. If you mirror an identical copy under the Pulsar model repo, use that URL instead.

Serve

vllm serve MultiverseComputingCAI/Pulsar-16B-BF16 \
  --served-model-name model \
  --max-num-seqs 8 \
  --tensor-parallel-size 1 \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3

Note: The NeMo container nvcr.io/nvidia/nemo:25.11.nemotron_3_nano comes with mamba_ssm and causal-conv1d pre-installed.

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Thinking (Reasoning) Control

Pulsar 16B supports a hybrid reasoning mode: the model can either think step-by-step before answering (reasoning mode) or reply directly (non-reasoning mode). The behaviour is controlled via the enable_thinking flag in the chat template.

This section provides a brief overview of reasoning control in Pulsar 16B. For comprehensive details please see the official Nemotron-3 Nano-30B model card at: https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b/modelcard

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Transformers API

Pass enable_thinking through apply_chat_template:

Thinking ON (default)

tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    enable_thinking=True,   # default — can be omitted
)

Thinking OFF

tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    enable_thinking=False,
)

When thinking is ON the model opens a <think> block before the answer.

output = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Split on </think> to separate reasoning from the final answer
if "</think>" in output:
    reasoning, answer = output.split("</think>", 1)
    reasoning = reasoning.replace("<think>", "").strip()
    answer = answer.strip()
else:
    answer = output

---

vLLM

Server-level default

Set the default for all requests at startup with --default-chat-template-kwargs.

Requires recent versions of vLLM.

Thinking OFF for all requests

vllm serve MultiverseComputingCAI/Pulsar-16B-BF16 \
  --served-model-name model \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3 \
  --trust-request-chat-template \
  --default-chat-template-kwargs '{"enable_thinking": false}' \
  ...

Thinking ON for all requests (default if flag is omitted)

vllm serve MultiverseComputingCAI/Pulsar-16B-BF16 \
  --served-model-name model \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3 \
  --trust-request-chat-template \
  --default-chat-template-kwargs '{"enable_thinking": true}' \
  ...

---

Per-request override

--trust-request-chat-template is required to allow per-request overrides.

Individual requests can override the server default by passing chat_template_kwargs in the request body. This works regardless of the server-level default.

Thinking ON/OFF for one request

import requests

response = requests.post("http://localhost:8000/v1/chat/completions", json={
    "model": "model",
    "messages": [{"role": "user", "content": "Solve: x² - 5x + 6 = 0"}],
    "max_tokens": 1024,
    "temperature": 1.0,
    "chat_template_kwargs": {"enable_thinking": True},
})

---

Tool Calling

Pulsar 16B emits tool calls in the following format:

<tool_call>
<function=get_weather>
<parameter=city>Paris</parameter>
<parameter=unit>celsius</parameter>
</function>
</tool_call>

When serving (e.g with vLLM), you must use the qwen3_coder tool parser.

vllm serve <model_path> \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --trust-remote-code

Training & Fine-Tuning

Base Model: NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

The base model nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. The model's reasoning capabilities can be configured through a flag in the chat template. See the original model card for details.

CompactifAI Compression

CompactifAI was applied to produce a smaller, efficient model (16B parameters) while aiming to preserve reasoning and tool-use capabilities. Supervised Fine Tuning was applied for improving cabapilities.

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Evaluation & Benchmarks

Quantizations

• BF16
• FP8
• NVFP4

Performance

• Framework: guidellm
• Inference: vLLM 0.18.0
• GPU: NVIDIA L40s
• Decode: temperature: 0.0, top_p: 1.0
• Measure Window: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
• Workload shape: 8k/16k workload as in the original model's card.

Long Context

Pulsar 16B preserves strong long-context behavior after compression, tracking the Nemotron-3-Nano-30B-A3B baseline closely across retrieval-heavy and full-suite long-context evaluations. Results are reported for LongBench v1, AA-LCR, NIAH, and RULER groupings up to 256k context.

Evaluation Methodology

Benchmark scores were obtained with the following setups. Methodology varies by benchmark family.

Inference:

• Backend: VLLM 0.18.0
• Nemotron models: temp 1.0, top_p 1.0
• GPT-OSS-20B: temp: 1.0, top_p: 1.0, reasoning_effort: high
• Qwen3-14B: temp: 0.6, top_p: 0.95, top_k: 20, min_p: 0.0
• Ministral-3-14B-Instruct-2512: temp: 0.15

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Languages

• Primary language: English
• Other languages: Spanish

Trained mainly on English with added Spanish. No systematic evaluation for languages outside English and Spanish.

Safety & Limitations

Known Limitations

• English-centric training data (inherited from base model).
• Tool calling depends on correct schema and tool design; exact parity with the original model is not guaranteed.
• Compression may affect some behaviors; evaluate for your use case.

Recommendations

• Validate tool outputs before running them
• Human oversight for critical use
• Task-specific eval before production

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Model Information

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Citation

If you use this model, please cite the base model and Pulsar 16B:

@misc{nemotron3nanoTR,
  title         = {NVIDIA Nemotron 3 Nano Technical Report},
  author        = {{NVIDIA}},
  year          = {2025},
  url           = {https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf}
}
@misc{nemotron3nanoslim16b,
  title         = {Pulsar 16B: Model developed from NVIDIA Nemotron-3-Nano-30B-A3B},
  author        = {Multiverse Computing},
  year          = {2026},
  url           = {https://huggingface.co/MultiverseComputingCAI/Pulsar-16B-BF16},
  note          = {Model developed based on nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 using CompactifAI technology}
}

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