ContextQMD
Back to Transformers

ZAYA

docs/source/en/model_doc/zaya.md

5.13.02.0 KB
Original Source
<!--Copyright 2026 the HuggingFace Inc. team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. -->

This model was contributed to Hugging Face Transformers on 2026-07-01.

ZAYA

Overview

ZAYA1 is a 760M active / 8.4B total parameter MoE language model trained by Zyphra. It combines Compressed Convolutional Attention (CCA), a nonlinear ZAYA1 router, and residual scaling.

ZAYA1 uses the Gemma 3 tokenizer. For more details, see the ZAYA1 model card and Zyphra's technical reports.

This model was contributed by JJJYmmm.

Usage examples

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Zyphra/ZAYA1-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Write a haiku about recursion in programming."}],
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
)
inputs = inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

ZayaConfig

[[autodoc]] ZayaConfig

ZayaModel

[[autodoc]] ZayaModel - forward

ZayaForCausalLM

[[autodoc]] ZayaForCausalLM - forward