Vulpem Ventures

BTCPay Server 2.3 is out! 🚨 Starting today, you can charge merchants a subscription fee to use your BTCPay Server, making it sustainable for local enthusiasts to onboard businesses to accept Bitcoin while undercutting credit card fees and preserving merchant self-custody.

We're excited to release one of the smallest and most performant Guardrail Models ever- MiniGuard-v0.1: A 0.6B parameter model that achieves performance at par with Nemotron-8B while being 13x smaller. We looked at where large models beat small ones, and it’s not general reasoning. It was trigger words like "kill" or "shoot" showing up in safe contexts. "Kill the process" vs "kill him." "Shoot the photo" vs "shoot the target." To account for these, we trained Miniguard-v0.1 using four techniques, each targeting a specific gap between small and large model performance: 1. Targeted synthetic data 2. Step-by-step distillation (Reasoning Data) 3. Model soup 4. FP8 quantization The Results: On the Nemotron-Safety-Guard benchmark (English test split) - MiniGuard achieves 0.893 Macro F1 - Nemotron-Guard-8B achieves 0.897 Macro F1. 99.5% of the accuracy at 1/13th the size. At typical production concurrency (1-8 requests), MiniGuard is 2-2.5x faster than Nemotron. MiniGuard-v0.1 is available now under the MIT license. It's a drop-in replacement for Nemotron Guard. Same prompt template, same output format. Head on over to our HuggingFace repo to read how we built the model and try it out yourself 👇

Today we’re open-sourcing Funcdex - the complete framework for building your own function-calling models! With Funcdex, you can build LLMs as small as 600M parameters for multi-turn function-calling on your specific set of tools. We expect these models to outperform frontier models - consistently. We're open-sourcing everything you need to build today - code, datasets & fine-tuned examples. Here’s what’s in the release: (1) Funcdex-MT-Function-Calling: A dataset with over 100k multi-turn function calling examples across 15 toolkit configurations. One of the largest publicly available datasets. (2) Funcdex-Synthesizer: A complete pipeline that converts your OpenAPI specs into toolkit-specific LLM training data with agent traces and tool use patterns. (3) Funcdex-Models: Our proof-of-concept fine-tunes of Qwen3 (0.6B & 1.7B), showing how efficient models can get when you optimize for specific APIs rather than general utilisation. Our smallest model achieves an average score of 0.7 function-call string match at $0.19 per eval, while GPT-5 Mini achieves 0.58 at $99.71. This validates our thesis that specialized small models beat general large models when you have narrow use cases and elaborate system prompts. While our Funcdex-MT-Function-Calling dataset covers Gmail, Calendar, Drive, Jira, Slack, and 10+ other business tools in both single and multi-toolkit scenarios, the included synthesizer has all the code and tutorials to generate your own training data from any OpenAPI spec. We truly believe that while generally intelligence models are good for most tasks, building task-specific models offers a more sustainable and sovereign approach. Link to code, datasets, and models below 👇