Instructions to use Nimbus-Labs/Nimbus-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nimbus-Labs/Nimbus-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Nimbus-Labs/Nimbus-2B")
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-2B")
model = AutoModelForMultimodalLM.from_pretrained("Nimbus-Labs/Nimbus-2B", 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-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nimbus-Labs/Nimbus-2B"
# 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-2B",
		"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-2B

SGLang

How to use Nimbus-Labs/Nimbus-2B 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-2B" \
    --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-2B",
		"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-2B" \
        --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-2B",
		"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-2B with Docker Model Runner:
```
docker model run hf.co/Nimbus-Labs/Nimbus-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nimbus-2B

A fast local coding assistant built for low-latency drafting, repair, and focused repository tasks.

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`	`Nimbus-Labs/Nimbus-2B-GGUF`	`Nimbus-Labs/Nimbus-2B-MLX-5bit`
Nimbus-4B	Balanced implementation and debugging	`Nimbus-Labs/Nimbus-4B`	`Nimbus-Labs/Nimbus-4B-GGUF`	`Nimbus-Labs/Nimbus-4B-MLX-5bit`
Nimbus-9B v2.1	Deeper coding and reasoning	`Nimbus-Labs/Nimbus-9B-v2.1`	`Nimbus-Labs/Nimbus-9B-v2.1-GGUF`	`Nimbus-Labs/Nimbus-9B-v2.1-MLX-5bit`

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	3	4,426,558,168 (4.43 GB)	`SHA256SUMS`

Text-only local files: Nimbus-Labs/Nimbus-2B-GGUF

Evaluation

No public score is claimed in this card yet. Results will be added only after the exact released artifact, complete task set, and scorer outputs are bound to a release manifest.

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.

from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "Nimbus-Labs/Nimbus-2B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

Training and provenance

Immediate upstream: Qwen/Qwen3.5-2B-Base
Pinned revision: b1485b2fa6dfa1287294f269f5fb618e03d52d7c
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, THIRD_PARTY_NOTICES.md, and the bundled Apache-2.0 text. The model is a derivative distribution; Nimbus attribution does not replace upstream attribution.