Instructions to use Lorenzob/synch-2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lorenzob/synch-2-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Lorenzob/synch-2-merged", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lorenzob/synch-2-merged", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Lorenzob/synch-2-merged", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lorenzob/synch-2-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lorenzob/synch-2-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lorenzob/synch-2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lorenzob/synch-2-merged

SGLang

How to use Lorenzob/synch-2-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Lorenzob/synch-2-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lorenzob/synch-2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Lorenzob/synch-2-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lorenzob/synch-2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lorenzob/synch-2-merged with Docker Model Runner:
```
docker model run hf.co/Lorenzob/synch-2-merged
```

synch-2-merged / README.md

Lorenzob

fix: model-index schema (dataset block) for HF validator

ac9cc77 verified 3 months ago

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metadata

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
  - merged
  - nemotron-h
  - conversational
  - custom_code
  - endpoints-ready
  - tgi-compatible
  - vllm-compatible
inference:
  parameters:
    temperature: 0.7
    top_p: 0.9
    max_new_tokens: 512
    do_sample: true
widget:
  - messages:
      - role: system
        content: You are sync2, a reasoner trained with Fractal RL.
      - role: user
        content: Spiega il principio di Ollivier-Ricci in 3 punti.
    example_title: Reasoning · IT
  - messages:
      - role: user
        content: >-
          Write a Python function that computes Ollivier-Ricci curvature on a
          graph.
    example_title: Code · EN
model-index:
  - name: Lorenzob/synch-2-merged
    results:
      - task:
          type: text-generation
          name: Cognitive Reasoning · Fractal-RL Composite
        dataset:
          type: internal
          name: Synch2 Internal Eval (private)
        metrics:
          - type: fractal_rl_reward
            value: 0.2
            name: Best Fractal-RL Composite Reward
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-merged

Full merged model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (120B MoE · 12B active) + Lorenzob/synch-2 (v11 APOGEO LoRA · best reward +0.200).

Drop-in compatibile con: HF Dedicated Endpoints, TGI, vLLM, HF Inference API, Together AI, Modal, RunPod, Replicate.

Quickstart · Dedicated Endpoint

from huggingface_hub import InferenceClient
client = InferenceClient("Lorenzob/synch-2-merged", token="<HF_TOKEN>")
out = client.chat_completion(
    messages=[
        {"role": "user",
         "content": "Compute the Ollivier-Ricci curvature of K_5."},
    ],
    max_tokens=512, temperature=0.7,
)
print(out.choices[0].message.content)

Quickstart · TGI (Text Generation Inference)

docker run --gpus all --shm-size 1g -p 8080:80 \
    -v $PWD/data:/data \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id Lorenzob/synch-2-merged --trust-remote-code \
    --num-shard 4 --max-input-length 4096 --max-total-tokens 8192

Quickstart · vLLM

python -m vllm.entrypoints.openai.api_server \
    --model Lorenzob/synch-2-merged --trust-remote-code \
    --dtype bfloat16 --tensor-parallel-size 4 \
    --max-model-len 8192

Quickstart · Locale (transformers full-weights)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained(
    "Lorenzob/synch-2-merged", trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "Lorenzob/synch-2-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

Suggested Hardware

AWS · 4x H100 80GB (consigliato) — accetta 8x A100 80GB — pesi BF16 totali ~245 GB, 50 shard model-XXXXX-of-00050.safetensors.

Merge Details

Base: NVIDIA Nemotron-3-Super-120B-A12B-BF16 (120B MoE, 12B active)
Adapter: Lorenzob/synch-2 (v11 APOGEO, best reward +0.200)
LoRA rank: 64 · LoRA alpha: 32
Merge type: weight addition (peft merge_and_unload)
Attention impl: SDPA (default), FlashAttention-2 supported

Governance

Vedi i documenti dedicati nel repo:

bias.md — bias analysis
safety.md — safety considerations
privacy.md — privacy implications
explainability.md — interpretability notes
accuracy_chart.png — eval results visual

Attribution

Cognitive behaviors: Gandhi et al. 2025 (arXiv:2503.01307)
Self-improving reasoner: karpathy/nanochat
Fractal RL · LCTR · THESIA: Lorenzo Bernardini publications.

License

Apache-2.0 (this merged model) · NVIDIA Nemotron-3 license applies to the underlying base weights distribution.