---
license: apache-2.0
language:
  - en
  - it
base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
library_name: transformers
pipeline_tag: text-generation
tags:
  - nemotron_h
  - sync2
  - fractal-rl
  - cognitive-behaviors
  - self-improving
  - thesia
  - alignment
  - lora
  - peft
  - custom_code
  - endpoints-ready
inference:
  parameters:
    temperature: 0.7
    top_p: 0.9
    max_new_tokens: 512
    do_sample: true
widget:
  - text: |
      <|system|>You are sync2, a reasoner trained with Fractal RL.
      <|user|>Spiega in 3 punti il principio di Ollivier-Ricci.
      <|assistant|>
    example_title: Reasoning · IT
  - text: |
      <|system|>You are sync2.
      <|user|>Write a Python function for Ricci curvature on a graph.
      <|assistant|>
    example_title: Code · EN
extra_gated_prompt: >-
  Accept the Apache-2.0 license and the NVIDIA Nemotron-3 base
  license. The model embeds Lorenzo Bernardini's Fractal-RL / THESIA
  research; cite arxiv:2503.01307 for cognitive-behaviors methodology.
extra_gated_fields:
  Affiliation: text
  Country: country
  Intended use: text
---
# Lorenzob/synch-2

**Base model**: `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16` (120B MoE · 12B active)
**Variant**: adapter-only (LoRA) · **Final checkpoint**: `sync2_apogeo_si_iter1`

Ready for **HuggingFace Dedicated Inference Endpoints**, HF Inference API,
Together AI, Modal, vLLM/TGI compatible runtimes.

## Quickstart · Dedicated Endpoint
```python
from huggingface_hub import InferenceClient
client = InferenceClient("Lorenzob/synch-2", token="<HF_TOKEN>")
out = client.text_generation(
    "<|user|>Compute the Ollivier-Ricci curvature of K_5.<|assistant|>",
    max_new_tokens=512, temperature=0.7,
)
print(out)
```

## Quickstart · Local (PEFT)
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Lorenzob/synch-2",
                                    trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", torch_dtype=torch.bfloat16,
    device_map="auto", trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "Lorenzob/synch-2")
```

## Suggested Hardware
AWS · 8x H100 80GB (o equivalente A100 80GB x4) — `bfloat16` adapter + base = ~245 GB VRAM.

## Training Pipeline
1. **SFT** — 9-dataset supervised fine-tuning (KIMI-K2.5, Opus-4.6-Reasoning,
   coding-200k, tulu-3-math, multilingual, Nettoov, MLEM, tool-reasoning).
2. **EML-RLHF** — symbolic regression reward (EML trees).
3. **Fractal RL** — GRPO con 8-component reward (coherence + fractal +
   Ollivier-Ricci curvature + EML + math + reasoning + length + DPP).
4. **v11 APOGEO** — alignment SFT (300 step) + self-improving reasoner
   (2 iter × 80 prompt × top-30%). Best reward `+0.200`.

## Attribution
- Cognitive behaviors: Gandhi et al. 2025 ([arXiv:2503.01307](https://arxiv.org/abs/2503.01307))
- Self-improving reasoner: [karpathy/nanochat](https://github.com/karpathy/nanochat)
- Fractal RL · LCTR · THESIA: Lorenzo Bernardini publications.

## License
Apache-2.0 (this adapter) · NVIDIA Nemotron-3 license (base weights).