Instructions to use sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e with Transformers:

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

pipe = pipeline("text-generation", model="sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e", 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("sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e", 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 sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e

SGLang

How to use sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e 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 "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e" \
    --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": "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e",
		"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 "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e" \
        --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": "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e with Docker Model Runner:
```
docker model run hf.co/sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e
```

Nemotron-3-Nano-30B-A3B — REAP-pruned to 64 experts

A 50% expert-pruned version of nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B, produced with REAP (Router-weighted Expert Activation Pruning). Half the routed experts in each MoE layer were removed, shrinking the model from ~59 GB to ~32 GB while keeping the same routing width.

	Base	This model
Routed experts / MoE layer	128	64
Experts activated per token	6	6 (unchanged)
Total params	~31.6 B	~31.6 B nominal (fewer experts stored)
Size (BF16)	~59 GB	~32 GB
Architecture	`nemotron_h` (hybrid Mamba-Transformer MoE)	same

What is REAP?

REAP is a one-shot, post-training method (no retraining) that scores every expert by the mean of router_weight × activation_norm over a calibration set, then removes the least-salient experts. Because only a few experts fire per token, many are redundant — pruning them reduces the model's memory footprint. Note that experts-per-token is unchanged (6), so the active compute per token is the same; the win is total size / memory, not decode speed.

How this was made

Method: REAP, layer-wise (block-by-block) calibration.
Compression: compression_ratio 0.5 → 64 of 128 routed experts kept per MoE layer.
Calibration: 256 samples from theblackcat102/evol-codealpaca-v1 at 512-token context.
Hardware: pruned on an NVIDIA DGX Spark (GB10).

Caveats

This is a moderate-calibration prune (256 samples). It generates coherent, on-task text in sanity checks, but has not been run through formal benchmarks — expect some quality regression vs the base model, especially outside the coding/reasoning calibration domain. A production prune would use a larger, more diverse calibration set.
nemotron_h includes Mamba layers. With trust_remote_code=False (native transformers modeling), it runs on a pure-torch fallback; install mamba-ssm + causal-conv1d for the fast path.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo = "sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e"  # this repo
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="cuda"
).eval()

msgs = [{"role": "user", "content": "Write a Python function for the nth Fibonacci number."}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
out = model.generate(**inputs, max_new_tokens=256)
print(tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

Inherits the base model's license (NVIDIA Open Model License). This is a derivative of nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B.

Downloads last month: 474

Safetensors

Model size

17B params

Tensor type

F32

BF16

Model tree for sapidlabs/Nemotron-3-Nano-30B-A3B-REAP-64e

Base model

nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Finetuned

(52)

this model