Fastino Labs

In collaboration with @nvidia we're releasing two new open weight models: Fastino-Nemotron-3.5-Lightning-Finance and Fastino-Nemotron-3.5-Lightning-Healthcare.

Working closely with the Nemotron team, we developed both models on Nemotron 3.5 Lightning using the Fastino Fine-Tuning Agent, which autonomously ran the entire post-training pipeline. The base model was highly responsive, with the agent reaching substantial gains across both domains with compact datasets alone.

- Fastino-Nemotron-3.5-Lightning-Finance excels at numerical reasoning, research, and summarization over financial concepts and documents. When evaluated, the fine-tuned model increased FinQA execution accuracy by 43.37 points (15.86% to 59.23%), and increased 7.81 points (from 49.65% to 57.46%) on BizFinBench.

- Fastino-Nemotron-3.5-Lightning-Healthcare specializes in clinical conversation quality, summarizing notes and discharge documentation, and extracting medical concepts from unstructured text. In post-training, the Fastino Fine-Tuning Agent improved performance by a margin of 4.28 points on HealthAdminBench (from 25.67% to 29.95%) and 5.09 points on MedCalc-Bench (from 49.09% to 54.18%). It also increased flag accuracy on MEDEC by 11.32 points, beating Opus 4.6 and beating Muse Spark by 0.98 points.

Both models are available on @huggingface under the Apache 2.0 license:
- huggingface.co/fastino/Fastin…
- huggingface.co/fastino/Fastin…

Today we are also releasing the Fastino Fine-Tuning Agent in private preview, which you can sign up for access to here: fastino.ai/nvidia-collabo…

When it comes to AI privacy and security, smaller models may be better suited for the task.

Every interaction with a model introduces potential security risks. Guardrail models are able to detect these risks by classifying inputs and outputs for safety violations, such as harmful content and prompt injection attempts.

Our founding researcher, @urchadeDS, explains how small, customized encoders like GLiGuard can perform these checks quickly, without the overhead of generating responses or reasoning tokens.

Watch the full conversation: youtu.be/6t6pZXsQXas?si…

Download GLiGuard: huggingface.co/collections/fa…

Introducing the GLiDE family: five open-weight decision models, from 0.4B to 27B.

Together, they set a new Pareto frontier for decision models. On @huggingface Decision Index, each model is expected to score higher than alternatives with the same or fewer parameters in this comparison.

- GLiDE 2B: 45.5 vs. 40.11 for StartLux 2B (+13.4%)
- GLiDE 9B: 60.6 vs. 60.26 for Baseten elephant-1
- GLiDE 27B: 73.3 vs. 71.86 for KnowLine 31B, with approximately 11% fewer served parameters

Every model returns a choice and a probability for each option in one forward pass, with no reasoning tokens.

All five share the System One schema, so you can switch sizes without changing your questions or how you parse the answers.

Weights: huggingface.co/collections/fa…
API: agent.fastino.ai
Blog: fastino.ai/blog/glide-dec…

A decision model that’s 90% confident isn’t necessarily right 90% of the time.

When a model is uncalibrated, the confidence score reflects how strongly it prefers a label, but not how often that prediction is correct.

Reinforcement Learning for Calibrated Decisions (RLCD) is a post-training technique that fixes this by rewarding models for making correct decisions and for expressing uncertainty accurately.

The result is calibrated probabilities, so teams know when to automate a decision and when to escalate uncertain responses to a human reviewer.

In our latest blog, we break down how RLCD works, why calibration matters for decision-making, and when to apply it to your own models.

Read the full blog: fastino.ai/blog/rlcd-trai…

LLMs are powerful, but they’re designed primarily for generating text rather than decision-making.

Decision models are fast models that evaluate a given situation and return a label, choice, or confidence score used to take quick, decisive actions.

With so many decision models available, it can be hard to know which one is right for your use case.

We’ve broken the selection process down into 4 steps:
1. Define your possible outcomes, including options for uncertain cases.
2. Shortlist models based on task requirements and benchmark performance.
3. Compare accuracy, speed, and cost using the same test cases.
4. Set confidence thresholds to determine when human review is needed.

In our recent blog, we break down popular use cases, some of the top decision models, and when you should train them.

Read the full guide: fastino.ai/blog/decision-…