Instructions to use Nimbus-Labs/Nimbus-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nimbus-Labs/Nimbus-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Nimbus-Labs/Nimbus-4B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Nimbus-Labs/Nimbus-4B")
model = AutoModelForMultimodalLM.from_pretrained("Nimbus-Labs/Nimbus-4B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nimbus-Labs/Nimbus-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nimbus-Labs/Nimbus-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nimbus-Labs/Nimbus-4B",
		"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/Nimbus-Labs/Nimbus-4B

SGLang

How to use Nimbus-Labs/Nimbus-4B 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 "Nimbus-Labs/Nimbus-4B" \
    --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": "Nimbus-Labs/Nimbus-4B",
		"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 "Nimbus-Labs/Nimbus-4B" \
        --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": "Nimbus-Labs/Nimbus-4B",
		"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 Nimbus-Labs/Nimbus-4B with Docker Model Runner:
```
docker model run hf.co/Nimbus-Labs/Nimbus-4B
```

Nimbus-4B / README.md

Nimbus8-Labs

Add MLX-5bit column to family table (separate from GGUF)

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	---
	language:
	- en
	license: other
	license_name: nimbus-layered-open-source
	license_link: https://huggingface.co/Nimbus-Labs/Nimbus-4B/blob/main/LICENSES.md
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.5-4B-Base
	base_model_relation: finetune
	tags:
	- nimbus
	- qwen3.5
	- coding
	- reasoning
	- local
	- transformers
	---

	![Nimbus Labs local coding models](assets/nimbus-labs-social.svg)

	# Nimbus-4B

	A balanced local coding model for implementation, debugging, and bounded agentic workflows.

	Nimbus is a family of local coding models: 2B for speed, 4B for balance, and 9B v2.1 for deeper reasoning. This Transformers repository contains the merged BF16 checkpoint and full multimodal components. The corresponding GGUF repository is text-only.

	## Choose a Nimbus model

	\| Model \| Best fit \| Transformers \| GGUF \| MLX \|
	\| --- \| --- \| --- \| --- \| --- \|
	\| Nimbus-2B \| Fast drafting and focused edits \| [`Nimbus-Labs/Nimbus-2B`](https://huggingface.co/Nimbus-Labs/Nimbus-2B) \| [`Nimbus-Labs/Nimbus-2B-GGUF`](https://huggingface.co/Nimbus-Labs/Nimbus-2B-GGUF) \| [`Nimbus-Labs/Nimbus-2B-MLX-5bit`](https://huggingface.co/Nimbus-Labs/Nimbus-2B-MLX-5bit) \|
	\| Nimbus-4B \| Balanced implementation and debugging \| [`Nimbus-Labs/Nimbus-4B`](https://huggingface.co/Nimbus-Labs/Nimbus-4B) \| [`Nimbus-Labs/Nimbus-4B-GGUF`](https://huggingface.co/Nimbus-Labs/Nimbus-4B-GGUF) \| [`Nimbus-Labs/Nimbus-4B-MLX-5bit`](https://huggingface.co/Nimbus-Labs/Nimbus-4B-MLX-5bit) \|
	\| Nimbus-9B v2.1 \| Deeper coding and reasoning \| [`Nimbus-Labs/Nimbus-9B-v2.1`](https://huggingface.co/Nimbus-Labs/Nimbus-9B-v2.1) \| [`Nimbus-Labs/Nimbus-9B-v2.1-GGUF`](https://huggingface.co/Nimbus-Labs/Nimbus-9B-v2.1-GGUF) \| [`Nimbus-Labs/Nimbus-9B-v2.1-MLX-5bit`](https://huggingface.co/Nimbus-Labs/Nimbus-9B-v2.1-MLX-5bit) \|

	![Nimbus family download footprint](assets/nimbus-family-footprint.png)

	The adjacent `assets/nimbus-family-footprint.json` contains the plotted values. Download size is not runtime memory: context cache and runtime buffers require additional capacity.


	## Artifacts

	\| Format \| Shards \| Weight bytes \| Integrity \|
	\| --- \| ---: \| ---: \| --- \|
	\| BF16 safetensors \| 5 \| 9,078,619,688 (9.08 GB) \| `SHA256SUMS` \|

	Text-only local files: [`Nimbus-Labs/Nimbus-4B-GGUF`](https://huggingface.co/Nimbus-Labs/Nimbus-4B-GGUF)

	## Evaluation

	The released Q5_K_M artifact was evaluated in direct mode on the full HumanEval and MBPP suites with llama.cpp `b10007`, one answer per task, temperature 0.6, top-p 0.95, top-k 20, and seed 42.

	\| Benchmark \| Passed \| Total \| pass@1 \|
	\| --- \| ---: \| ---: \| ---: \|
	\| HumanEval \| 121 \| 164 \| 73.8% \|
	\| HumanEval+ \| 112 \| 164 \| 68.3% \|
	\| MBPP \| 285 \| 378 \| 75.4% \|
	\| MBPP+ \| 232 \| 378 \| 61.4% \|

	![Nimbus-4B EvalPlus results](assets/nimbus-4b-evalplus.png)

	The adjacent `assets/nimbus-4b-evalplus.json` is the machine-readable source for this chart.



	## Quickstart

	Use a recent Transformers release compatible with Qwen3.5. Load the repository with `trust_remote_code=False`, preserve the supplied processor/tokenizer files, and enable thinking through the supplied chat template where supported. Validate generated code before execution.

	```python
	from transformers import AutoProcessor, AutoModelForImageTextToText

	model_id = "Nimbus-Labs/Nimbus-4B"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
	```

	## Training and provenance

	- Immediate upstream: [`Qwen/Qwen3.5-4B-Base`](https://huggingface.co/Qwen/Qwen3.5-4B-Base)
	- Pinned revision: `1001bb4d826a52d1f399e183466143f4da7b741b`
	- Foundation: Qwen3.5
	- Relationship: Nimbus post-training / fine-tune and verified merge
	- Exact uploaded file hashes: `SHA256SUMS`
	- Release metadata: `release-manifest.json`

	Nimbus prominently credits Qwen3.5 as the foundation for this model family. Nimbus-9B-v2.1 additionally credits DeepReinforce's Ornith-1.0-9B as its immediate upstream.

	## Intended use

	Local coding assistance, code explanation, debugging, test generation, and bounded tool-aware workflows. Host applications must enforce permissions, sandboxing, timeouts, and verification.

	## Limitations

	The model can produce incorrect, insecure, incomplete, or non-compiling code. Benchmark performance does not guarantee project-level correctness. GGUF artifacts are text-only even though this Transformers checkpoint includes multimodal components.

	## Licenses and notices

	See [`LICENSES.md`](LICENSES.md), [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md), and the bundled Apache-2.0 text. The model is a derivative distribution; Nimbus attribution does not replace upstream attribution.

	## Project

	[Nimbus8](https://nimbus8.app) Â· [GGUF files](https://huggingface.co/Nimbus-Labs/Nimbus-4B-GGUF)