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Akhil-Theerthala's
post
with ❤️
1 day ago
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!
You can check these out on the following links:
Models:
- https://huggingface.co/Akhil-Theerthala/Kuvera-8B-qwen3-v0.2.1
- https://huggingface.co/Akhil-Theerthala/Kuvera-4B-unsloth-gemma3
- https://huggingface.co/Akhil-Theerthala/kuvera-12B-v0.2.0-unsloth-gemma3
Dataset:
- https://huggingface.co/datasets/Akhil-Theerthala/Kuvera-PersonalFinance-V2.1
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!
posted
an
update
2 days ago
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 newest versions of DAG Reasoning are available now!
Get gpt-oss-20b: https://huggingface.co/sequelbox/gpt-oss-20b-DAG-Reasoning
Get Qwen3-4B-Thinking-2507: https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
You can also get the DAG Reasoning dataset, to train your own models to use DAG Reasoning Format: https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528
Support our experimental open-source research efforts, models and datasets: https://huggingface.co/spaces/sequelbox/SupportOpenSource
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!
with love and appreciation,
allegra
updated
a model
2 days ago
sequelbox/gpt-oss-20b-DAG-Reasoning
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