Fastino Nemotron 3.5 Super VL Healthcare

Fastino-Nemotron-3.5-Super-VL-Healthcare is a 120B-parameter, 12B-active hybrid Mamba-Transformer mixture-of-experts model specialized for healthcare and biomedical applications. The repository contains a ready-to-run merged checkpoint: the language backbone of the NVIDIA Nemotron 3.5 Super VL early-access checkpoint plus Fastino's combined-healthcare LoRA adapter. Users do not need to obtain or attach a separate LoRA.

• Developed by: Fastino Labs, in collaboration with NVIDIA
• Base checkpoint: NVIDIA Nemotron 3.5 Super VL, early-access checkpoint (language backbone)
• Post-training checkpoint: Fastino-Healthcare
• License: Apache 2.0
• Language: English
• Modalities: Text (the vision encoder of the VL base is not included)
• Fine-Tuning Agent: Agent sandbox
• Research: Fastino Fine-Tuning Agent

What it is designed for

The model targets biomedical question answering, clinical calculation, medical error detection and correction, biomedical concept, chemical, and disease extraction, and clinical note summarization.

Quickstart

The published weights are BF16 and require about 247 GB before runtime overhead. Tensor parallelism across four 80 GB or larger GPUs (or two B200s) is recommended.

pip install vllm  # a release with Nemotron 3.5 Super support

from vllm import LLM, SamplingParams

model_id = "fastino/Fastino-Nemotron-3.5-Super-VL-Healthcare"

llm = LLM(
    model=model_id,
    trust_remote_code=True,
    dtype="bfloat16",
    tensor_parallel_size=4,
    max_model_len=8192,
)

clinical_note = """65-year-old male with history of hypertension, type 2
diabetes, and CKD stage 3 presents with 2 weeks of progressive dyspnea on
exertion and orthopnea. Exam: bibasilar crackles, 3+ pitting edema to knees,
BP 152/94. Meds: lisinopril 20 mg daily, furosemide 40 mg daily. CXR:
cardiomegaly with pulmonary congestion. Assessment: acute decompensated
heart failure."""

messages = [
    {
        "role": "system",
        "content": "You are a clinical documentation assistant. Summarize notes accurately and concisely. Do not add information that is not in the note.",
    },
    {
        "role": "user",
        "content": f"Summarize the following clinical note in 3-5 bullet points:\n\n{clinical_note}",
    },
]

outputs = llm.chat(
    messages,
    SamplingParams(temperature=0.0, max_tokens=512),
)
print(outputs[0].outputs[0].text)

Post-training recipe

The data recipe was discovered by the Fastino Fine-Tuning Agent on NVIDIA Nemotron 3.5 Lightning, where it autonomously built evaluation sets, curated data, explored training mixtures and hyperparameters, evaluated transfer, and selected the winning mixture. For Super we ran no new search: the same mixture was trained once on the Super checkpoint through the Fastino Training API.

The adapter was trained on 11,776 de-duplicated examples covering:

• biomedical question answering over study abstracts;
• clinical calculation from patient notes;
• physician-style health conversations and evidence-grounded medical answers;
• medical error detection and correction;
• clinical dialogue summarization; and
• biomedical concept, chemical, and disease extraction.

It was trained from the base checkpoint for two epochs with LoRA rank 32 on attention, Mamba, latent-projection, and shared-expert weights (75.2M trainable parameters), learning rate 1.6e-4, batch size 32, and sequence packing disabled. Evaluation examples and labels were excluded from training.

Benchmark evaluation

Base and fine-tuned scores below use the same early-access checkpoint, inputs, prompts, decoding settings, inference route, and evaluator for each row.

In-domain benchmarks

Transfer to unseen benchmarks

Performance on related tasks the model was not explicitly trained for.

Evaluation protocol

Every benchmark uses a held-out set fixed before training and the same prompts and scorers as our Nemotron 3.5 Lightning study. Score-bearing comparisons were accepted only when base and candidate shared the same examples, prompt construction, decoding parameters, serving route, evaluator identity, and aggregation. Training mixtures were hashed and de-duplicated, and benchmark evaluation rows were excluded from training. Results are from a single training run.

Limitations

This is a specialized research model, not a medical device. Its outputs are not a substitute for professional clinical judgment. Clinical or other high-stakes deployment requires independent validation, appropriate safeguards, and qualified human oversight.

Fine-tuning

To fine-tune or serve models like this one without managing GPUs, see the Fastino API, which offers hosted training and inference.

Citation

@misc{atreja2026pioneeragentcontinualimprovement,
      title={Pioneer Agent: Continual Improvement of Small Language Models in Production},
      author={Dhruv Atreja and Julia White and Nikhil Nayak and Kelton Zhang and Henrijs Princis and George Hurn-Maloney and Ash Lewis and Urchade Zaratiana},
      year={2026},
      eprint={2604.09791},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.09791},
}

License

This model is licensed under the Apache License 2.0.

Links

• Fastino Labs
• Supporting research
• NVIDIA Nemotron 3.5 Super VL base model