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*This model was contributed to Hugging Face Transformers on 2026-10-13.*

# Ling 3.0

## Overview

Ling 3.0 Flash is a hybrid Mixture-of-Experts language model from InclusionAI. Its decoder groups five Kimi Delta
Attention (KDA) layers with one Multi-head Latent Attention (MLA) layer. The MLA blocks use interleaved rotary
embeddings and a head-wise output gate, while the feed-forward blocks combine routed and shared experts.

The native Transformers architecture is exposed as `BailingHybridForCausalLM` and uses the checkpoint model type
`bailing_hybrid`. Native support means the model can be loaded without `trust_remote_code=True`.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "inclusionAI/Ling-3.0-Flash"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="auto")

messages = [{"role": "user", "content": "Introduce the Ling model family."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(model.device)
output = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(output[0][inputs.input_ids.shape[1] :], skip_special_tokens=True))
```

The KDA layers include a pure PyTorch fallback. If `flash-linear-attention` and `causal-conv1d` are installed, their
optimized kernels are used automatically on CUDA. Installing `kernels` and passing `use_kernels=True` to
`from_pretrained` can enable compatible Hub kernels when available.

## BailingHybridConfig

[[autodoc]] BailingHybridConfig

## BailingHybridModel

[[autodoc]] BailingHybridModel
    - forward

## BailingHybridForCausalLM

[[autodoc]] BailingHybridForCausalLM
    - forward