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Nimbus-Labs/Nimbus-4B
Nimbus-4B is a image-text-to-text model from Nimbus-Labs. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Downloads · 30 days
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.safetensors9.1 GB · 100%
From the Hugging Face model README
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.
| 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.
| 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
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% |

The adjacent assets/nimbus-4b-evalplus.json is the machine-readable source for this chart.
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-4B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
Qwen/Qwen3.5-4B-Base1001bb4d826a52d1f399e183466143f4da7b741bSHA256SUMSrelease-manifest.jsonNimbus 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.
Local coding assistance, code explanation, debugging, test generation, and bounded tool-aware workflows. Host applications must enforce permissions, sandboxing, timeouts, and verification.
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.
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.