Instructions to use z-lab/Kimi-K2.5-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Kimi-K2.5-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Kimi-K2.5-DFlash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Kimi-K2.5-DFlash", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/Kimi-K2.5-DFlash", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/Kimi-K2.5-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Kimi-K2.5-DFlash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Kimi-K2.5-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Kimi-K2.5-DFlash

SGLang

How to use z-lab/Kimi-K2.5-DFlash 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 "z-lab/Kimi-K2.5-DFlash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Kimi-K2.5-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "z-lab/Kimi-K2.5-DFlash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Kimi-K2.5-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Kimi-K2.5-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/Kimi-K2.5-DFlash
```

Add diffusers model_index.json and scheduler config for DFlashPipeline.from_pretrained

by kashif HF Staff - opened 6 days ago

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

+13

-0

kashif

6 days ago

This adds the two files needed for diffusers to load the pipeline directly via DFlashPipeline.from_pretrained:

model_index.json — declares DFlashPipeline as the pipeline class and points
_target_model_name_or_path to the target/verifier model
scheduler/scheduler_config.json — config for DFlashTokenDiffusionScheduler

After merging, the pipeline can be loaded with:

import torch
from diffusers import DFlashPipeline

pipe = DFlashPipeline.from_pretrained(
    "z-lab/Kimi-K2.5-DFlash",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
out = pipe(prompt="Hello, world!")
print(out.texts[0])

Add diffusers model_index.json and scheduler config for DFlashPipeline.from_pretrained716f82d7

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