Instructions to use ProCreations/nib-4b-preview-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProCreations/nib-4b-preview-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ProCreations/nib-4b-preview-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ProCreations/nib-4b-preview-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("ProCreations/nib-4b-preview-NVFP4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProCreations/nib-4b-preview-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProCreations/nib-4b-preview-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": "ProCreations/nib-4b-preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ProCreations/nib-4b-preview-NVFP4

SGLang

How to use ProCreations/nib-4b-preview-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 "ProCreations/nib-4b-preview-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": "ProCreations/nib-4b-preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "ProCreations/nib-4b-preview-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": "ProCreations/nib-4b-preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ProCreations/nib-4b-preview-NVFP4 with Docker Model Runner:
```
docker model run hf.co/ProCreations/nib-4b-preview-NVFP4
```

Nib 4B (preview), NVFP4

NVFP4 weights of Nib 4B, made with llm-compressor 0.15 (GPTQ, group-16 e2m1 weights with e4m3 scales, calibrated activation scales) in compressed-tensors format. The attention and Gated DeltaNet projections and the last eight MLPs use FP8 instead (eight rather than four MLPs lowered mean KL by 12% for about 0.14 GB). 5.0 GB. Needs a GPU with FP4 support (NVIDIA Blackwell) for full speed.

Only the main linear projections are quantized. The vision encoder, embeddings and tied output head, all norms, the Gated DeltaNet gates, convolutions and decay parameters, and the MTP weights are kept byte-for-byte from the BF16 model (in nib-protected.safetensors).

	Mean KL vs BF16	p99 KL	Top-1 agreement	Browser tasks (581-task live set, paired)
NVFP4	0.0147	0.193	97.0%	45.6% vs 45.3% for BF16 (+0.3, 95% CI −3.1 to +3.8)

Fidelity is measured against the BF16 model on 5,070 held-out assistant tokens from 48 complete agent episodes (up to 32k tokens, ten harnesses), none used for calibration. Calibration used 128 text episodes (700k tokens, ten harnesses) plus eight episodes with screenshots.

Serving (tested: SGLang 0.5.21)

python -m sglang.launch_server \
  --model-path ProCreations/nib-4b-preview-NVFP4 --served-model-name nib-4b \
  --context-length 131072 --reasoning-parser qwen3 --tool-call-parser qwen3_coder \
  --mamba-ssm-dtype bfloat16 --mamba-radix-cache-strategy extra_buffer_lazy --trust-remote-code

Tested on an RTX PRO 6000 (Blackwell) with CUDA graphs, radix cache and 8 concurrent requests.

Reasoning effort

Same as Nib 4B: pass chat_template_kwargs {"enable_thinking": false} for thinking off, or {"enable_thinking": true, "reasoning_effort": "low" | "medium" | "xhigh"} (xhigh is the default). All our evaluations used temperature 1.0 and top_p 0.95.

About Nib

Nib is Qwen3.5-4B post-trained as a browser agent across common agent harnesses. Benchmarks, usage, training details and limitations (including that it often reports success on tasks it did not finish) are on the main model card. This is a preview release.

License: Apache 2.0, the same as the base model Qwen/Qwen3.5-4B.

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Safetensors

Model size

4B params

Tensor type

F8_E4M3

F32

BF16

Model tree for ProCreations/nib-4b-preview-NVFP4

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

ProCreations/nib-4b-preview

Quantized

(3)

this model