Text Generation
Transformers
Safetensors
qwen3
feature-extraction
dflash
speculative-decoding
diffusion
efficiency
flash-decoding
qwen
kimi
diffusion-language-model
custom_code
text-generation-inference
Instructions to use z-lab/Kimi-K2.5-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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, device_map="auto") - 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
Download config.json from z-lab/Kimi-K2.5-DFlash: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/z-lab/Kimi-K2.5-DFlash/resolve/main/config.json
- Command line
-
hf download hf://z-lab/Kimi-K2.5-DFlash/config.json
-
curl -L -o config.json https://huggingface.co/z-lab/Kimi-K2.5-DFlash/resolve/main/config.json
1.31 kB
| { | |
| "architectures": [ | |
| "DFlashDraftModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoModel": "dflash.DFlashDraftModel" | |
| }, | |
| "block_size": 8, | |
| "dflash_config": { | |
| "mask_token_id": 163838, | |
| "target_layer_ids": [ | |
| 1, | |
| 12, | |
| 24, | |
| 35, | |
| 47, | |
| 58 | |
| ] | |
| }, | |
| "dtype": "bfloat16", | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 7168, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 18432, | |
| "layer_types": [ | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention" | |
| ], | |
| "mask_token_id": 163838, | |
| "max_position_embeddings": 262144, | |
| "max_window_layers": 6, | |
| "model_type": "qwen3", | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 6, | |
| "num_key_value_heads": 8, | |
| "num_target_layers": 61, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 64.0, | |
| "mscale": 1.0, | |
| "mscale_all_dim": 1.0, | |
| "original_max_position_embeddings": 4096, | |
| "rope_type": "yarn", | |
| "type": "yarn" | |
| }, | |
| "rope_theta": 50000.0, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 163840 | |
| } | |