*This model was contributed to Hugging Face Transformers on 2026-07-03.* # KimiK-2.5, KimiK-2.6, KimiK-2.7 This model class supports all three different releases: KimiK-2.5,KimiK-2.6, KimiK-2.7 ## Overview Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in [Kimi K2.5: Visual Agentic Intelligence](https://www.kimi.com/en/blog/kimi-k2-5) and further improved in [Kimi K2.6: Advancing Open-Source Coding](Kimi K2.5: Visual Agentic Intelligence). Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization. The model is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision. This model was contributed by [RaushanTurganbay](https://huggingface.co/RaushanTurganbay). The offical checkpoints are [moonshotai/Kimi-K2.5](https://huggingface.co/moonshotai/Kimi-K2.5), [moonshotai/Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) and [moonshotai/Kimi-K2.7-Code](https://huggingface.co/moonshotai/Kimi-K2.7-Code). ## Usage examples Note that the repositories don't yet have the correct fast tokenizer uploaded. You can get the converted processor and tokenizer from [RaushanTurganbay/kimi2.7-processor](https://huggingface.co/RaushanTurganbay/kimi2.7-processor) ```python import os import torch from transformers import AutoProcessor, AutoTokenizer, AutoModelForImageTextToText from transformers.distributed.configuration_utils import DistributedConfig distributed_config = DistributedConfig(enable_expert_parallel=True) processor = AutoProcessor.from_pretrained('moonshotai/Kimi-K2.6') model = AutoModelForImageTextToText.from_pretrained( 'moonshotai/Kimi-K2.6', distributed_config=distributed_config, ) messages = [ { "role": "user", "content": [ {"type": "image", "image": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "What is shown in this image?"}, ], } ] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ).to(device=model.device, dtype=model.dtype) generated_ids = model.generate(**inputs, max_new_tokens=64) generated_text = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)[0] print(generated_text) ``` ## Kimi_K25ImageProcessor [[autodoc]] Kimi_K25ImageProcessor ## Kimi_K25Processor [[autodoc]] Kimi_K25Processor ## Kimi_K25VideoProcessor [[autodoc]] Kimi_K25VideoProcessor ## Kimi_K25Config [[autodoc]] Kimi_K25Config ## Kimi_K25VisionConfig [[autodoc]] Kimi_K25VisionConfig ## Kimi_K25PreTrainedModel [[autodoc]] Kimi_K25PreTrainedModel - forward ## Kimi_K25VisionModel [[autodoc]] Kimi_K25VisionModel ## Kimi_K25Model [[autodoc]] Kimi_K25Model - forward ## Kimi_K25ForConditionalGeneration [[autodoc]] Kimi_K25ForConditionalGeneration