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*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 official 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

<Tip warning={true}>

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)

</Tip> 

```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
