I recently worked on a LoRA that improves tool use in LLM. Thought the approach might interest folks here.
The issue I have had when trying to use some of the local LLMs with coding agents is this:
Me: "Find all API endpoints with authentication in this codebase" LLM: "You should look for @app.route decorators and check if they have auth middleware..."
But I often want it to search the files and show me but the LLM doesn't trigger a tool use call.
To fine-tune it for tool use I combined two data sources:
1. Magpie scenarios - 5000+ diverse tasks (bug hunting, refactoring, security audits) 2. Real execution - Ran these on actual repos (FastAPI, Django, React) to get authentic tool responses
This ensures the model learns both breadth (many scenarios) and depth (real tool behavior).
I'm excited to announce that I've just released the newest versions of my Kuvera models and the expanded Personal Finance Reasoning dataset on Hugging Face!
What's new: I've expanded the Personal Finance Reasoning Dataset, which now includes 18.9k samples of real-world financial questions paired with detailed, empathetic answers. The previous generation pipeline was also streamlined with better psychological context and response validations.
I've also released new Kuvera models trained on this improved dataset: - Kuvera-4B & 8B: These are my upgraded non-reasoning models, fine-tuned to provide practical financial advice. I've specifically trained the 8B model to better understand the user's emotional context. - Kuvera-12B: A first experimental reasoning model focused on the query resolution.
As the sole person working on this project, this release is a noticeable step forward from my previous work, offering more powerful and nuanced tools for financial AI.
I am actively looking to collaborate with others who are passionate about analyzing and improving the quality of personal finance advice generated by large language models. If this sounds like you, please reach out!
P.S. The paper on the framework used to generate these models along with the detailed evaluation of the main 8B model's responses is going to be released soon!
We've brought DAG Reasoning to gpt-oss-20b and Qwen3-4B-Thinking-2507!
- DAG Reasoning is the first model in our Experimental Reasoning Modalities series: use it to create structured, analytical Directed Acyclic Graphs to provide insight into your queries and situations! - Multi-step analysis identifies causal relationships, produces confidence measurements, and forms a single structured graph object. - DAG Reasoning Format provides clear, readable JSON containing structured, useful information; easy to use for creating visualizations, doing analysis, or further conversation with your assistant. - Trained in a variety of subjects for flexible analysis: programming, science, business, economics, finance, law, logistics, management, and more!
Our upcoming releases, coming soon with your support: - bringing Shining Valiant 3 to the Qwen 3 2507 series! - our next release in the Experimental Reasoning Modalities series - we're hard at work on this right now! - we'll be upgrading the Esper line with Esper 3.1 - newer and better datasets, asking tougher and deeper coding, DevOps, and architecture questions, plus improvements to general chat!