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sangue-e-grafi-nemotron-nano-sft-v7
cyberandy/sangue-e-grafi-nemotron-nano-sft-v7
sangue-e-grafi-nemotron-nano-sft-v7 is a text generation model from cyberandy. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
Supervised Fine-Tuned LoRA adapter on NVIDIA Nemotron Mini 4B — cross-architecture validation of the KG-agent approach.
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jun 13, 2026
- SHA
- d8e5b05c
Base models
Model card
From the Hugging Face model README
🩸 Sangue e Grafi — Nemotron Mini 4B SFT Adapter (v7)
<p align="center"> <img src="https://huggingface.co/spaces/cyberandy/sangue-e-grafi/resolve/main/banner.png" alt="Sangue e Grafi banner" width="700"/> </p>Supervised Fine-Tuned LoRA adapter on NVIDIA Nemotron Mini 4B — cross-architecture validation of the KG-agent approach.
Model Description
This is a LoRA (PEFT) adapter trained via Supervised Fine-Tuning (SFT) on top of nvidia/Nemotron-Mini-4B-Instruct. It serves as a cross-architecture baseline for the Sangue e Grafi project, demonstrating that the KG-agent approach generalizes beyond a single model family.
The adapter teaches the model the same skills as its Gemma counterpart:
- Parse complex kinship narratives in Italian
- Emit structured tool calls grounded in the OWL kinship ontology
- Reason step-by-step through multi-hop inheritance questions
Training Details
| Parameter | Value |
|---|---|
| Method | SFT (Supervised Fine-Tuning) |
| Base model | nvidia/Nemotron-Mini-4B-Instruct (4B params) |
| Training data | 500 adversarial kinship scenarios with teacher traces |
| Teacher | Gemini 2.5 Flash — generated gold reasoning traces |
| Format | SafeTensors LoRA adapter |
Benchmark Results 📊
Cross-Architecture Comparison
| Model | Hard Dev-Set Accuracy |
|---|---|
| Gemma 4B (SFT+GRPO) | 5/10 (50%) |
| Nemotron 4B (SFT+GRPO) | 4/10 (40%) |
| Gemini 2.5 Flash (no KG) | 3/10 (30%) — easy set |
Both 4B models with KG-agent outperform the frontier model without KG grounding — validating that the approach is architecture-agnostic.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")
model = PeftModel.from_pretrained(base, "cyberandy/sangue-e-grafi-nemotron-nano-sft-v7")
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")
Note: This is the SFT-only checkpoint. For the full pipeline, merge this adapter first, then apply the GRPO adapter.
Intended Uses & Limitations
Intended uses:
- Cross-architecture validation of KG-grounded reasoning
- Research on small-model tool-use agents
- Comparison with Gemma-based pipeline
Limitations:
- Trained only on Italian kinship / inheritance-law scenarios
- Requires the full KG agent framework for reported performance
- Slightly lower accuracy than Gemma variant on hard tasks
Project Links
| Resource | Link |
|---|---|
| 🚀 Live Demo | HF Space |
| 📦 GitHub | cyberandy/sangue-e-grafi |
| 📄 Paper | RLM-on-KG (arXiv:2604.17056) |
| 🎯 GRPO Adapter | sangue-e-grafi-nemotron-nano-grpo |
| 📊 Agent Traces Dataset | sangue-e-grafi-agent-traces |
Citation
@misc{sangue-e-grafi-2026,
title = {Sangue e Grafi: Small Models Beat Frontier LLMs on Adversarial Kinship Reasoning with Knowledge Graph Agents},
author = {Andrea Volpini},
year = {2026},
url = {https://github.com/cyberandy/sangue-e-grafi},
note = {Hugging Face Build Small Hackathon 2026}
}
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Languages
- it, en
- Created
- Jun 10, 2026
- Updated
- Jun 13, 2026