Instructions to use Nimbus-Labs/Nimbus-9B-v2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nimbus-Labs/Nimbus-9B-v2.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Nimbus-Labs/Nimbus-9B-v2.1")
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-9B-v2.1")
model = AutoModelForMultimodalLM.from_pretrained("Nimbus-Labs/Nimbus-9B-v2.1", 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-9B-v2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nimbus-Labs/Nimbus-9B-v2.1"
# 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-9B-v2.1",
		"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-9B-v2.1

SGLang

How to use Nimbus-Labs/Nimbus-9B-v2.1 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-9B-v2.1" \
    --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-9B-v2.1",
		"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-9B-v2.1" \
        --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-9B-v2.1",
		"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-9B-v2.1 with Docker Model Runner:
```
docker model run hf.co/Nimbus-Labs/Nimbus-9B-v2.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nimbus-9B-v2.1

A deeper local reasoning model for difficult coding, debugging, and tool-aware problem solving.

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	12	18,819,720,344 (18.82 GB)	`SHA256SUMS`

Text-only local files: Nimbus-Labs/Nimbus-9B-v2.1-GGUF

Evaluation

The released Q5_K_M artifact was evaluated with llama.cpp b10007 on Vulkan, native thinking, one answer per task, temperature 0.6, top-p 0.95, top-k 20, seed 42, and the official network-disabled EvalPlus scorer. The base run allowed 32,768 completion tokens. Each original length-plus-empty nonresponse received exactly one preserved 60,000-token recovery attempt; there was no repeated resampling.

Benchmark	Passed	Total	pass@1
HumanEval	146	164	89.0%
HumanEval+	135	164	82.3%
MBPP	330	378	87.3%
MBPP+	277	378	73.3%

Three HumanEval and seven MBPP truncations recovered at the larger limit. Two HumanEval and three MBPP nonresponses remained zero-valued. “Plus” uses EvalPlus' official base-and-plus pass condition.

The adjacent assets/nimbus-9b-v2.1-evalplus.json is the machine-readable source for this chart.

Release landscape

This is a capability and packaging comparison, not a cross-model score claim. Other publishers' benchmarks use different harnesses, prompts, inference budgets, and hardware, so they are not mixed into the Nimbus EvalPlus chart.

Release	Parameters	Modality	Published context	Positioning
Nimbus-9B v2.1	9B	Multimodal Transformers; text-only GGUF	262,144 validated for this release	Local coding/reasoning; BF16, Q4_K_M, Q5_K_M with checksums
Qwen3.5-9B	9B	Multimodal	262,144 in current serving guidance	Foundation model; reasoning and tool use
Ornith-1.0-9B	9B	Multimodal	262,144 in current serving guidance	Immediate upstream focused on coding agents
Granite-3.3-8B-Instruct	8B	Text	128,000	General instruction, code, function calling, and RAG

Nimbus-9B v2.1 inherits its architecture and foundation capabilities from Qwen3.5 through Ornith. Nimbus' contribution is the post-training, release packaging, quantization, and exact evaluation reported above—not authorship of the foundation model.

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-9B-v2.1"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

Training and provenance

Immediate upstream: deepreinforce-ai/Ornith-1.0-9B
Pinned revision: 83dc1f5e24ef8527af019a6b3bf66ac0f1c2c999
Foundation: Ornith-1.0-9B on Qwen3.5-9B
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.