Instructions to use unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", 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("unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", 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 unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4

SGLang

How to use unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 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 "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4" \
    --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": "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
		"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 "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4" \
        --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": "unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/unsloth/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4
```

danielhanchen commited on Jan 29

Commit

f27f0f4

verified ·

1 Parent(s): 694b35c

Update config.json

Browse files

Files changed (1) hide show

config.json +3 -8

config.json CHANGED Viewed

@@ -12,7 +12,6 @@
   "bos_token_id": 1,
   "chunk_size": 128,
   "conv_kernel": 4,
-  "torch_dtype": "bfloat16",
   "eos_token_id": 2,
   "expand": 2,
   "head_dim": 128,
@@ -44,7 +43,7 @@
   "num_hidden_layers": 52,
   "num_key_value_heads": 2,
   "num_logits_to_keep": 1,
-  "pad_token_id": 999,
   "partial_rotary_factor": 1.0,
   "rescale_prenorm_residual": true,
   "residual_in_fp32": false,
@@ -54,15 +53,11 @@
   "ssm_state_size": 128,
   "tie_word_embeddings": false,
   "time_step_floor": 0.0001,
-  "time_step_limit": [
-    0.0,
-    Infinity
-  ],
   "time_step_max": 0.1,
   "time_step_min": 0.001,
   "topk_group": 1,
-  "transformers_version": "4.57.6",
-  "unsloth_fixed": true,
   "use_bias": false,
   "use_cache": true,
   "use_conv_bias": true,

   "bos_token_id": 1,
   "chunk_size": 128,
   "conv_kernel": 4,
   "eos_token_id": 2,
   "expand": 2,
   "head_dim": 128,
   "num_hidden_layers": 52,
   "num_key_value_heads": 2,
   "num_logits_to_keep": 1,
+  "pad_token_id": 0,
   "partial_rotary_factor": 1.0,
   "rescale_prenorm_residual": true,
   "residual_in_fp32": false,
   "ssm_state_size": 128,
   "tie_word_embeddings": false,
   "time_step_floor": 0.0001,
   "time_step_max": 0.1,
   "time_step_min": 0.001,
   "topk_group": 1,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.2",
   "use_bias": false,
   "use_cache": true,
   "use_conv_bias": true,