language, license, library_name, pipeline_tag, tags, extra_gated_heading, extra_gated_button_content, extra_gated_prompt, extra_gated_fields

language	license	library_name	pipeline_tag	tags	extra_gated_heading	extra_gated_button_content	extra_gated_prompt	extra_gated_fields
multilingual am ar eu bn bg ca zh cs en fil fi fr de el ha he hi hu ig id ga it ja jv km ko lt ms mt no fa pl pa ru sk sw ta te th tr uk ur vi yo zu	cc-by-nc-4.0	transformers	text-generation	aya multilingual reasoning tiny-aya	Acknowledge license to accept the repository	Accept license	By submitting this form, you agree to the License Agreement and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere's Privacy Policy. You'll receive email updates about Cohere Labs and Cohere research, events, products and services. You can unsubscribe at any time.	Name Email Affiliation Country I agree to use this model for non-commercial use ONLY text text text country checkbox

Model Card for Tiny Aya En-Thinker

Model Summary

Cohere Labs Tiny Aya En-Thinker is an open-weights research release of a 3.35 billion parameter multilingual reasoning model. It is trained on multilingual reasoning data with English reasoning traces for 44 languages plus English, with coverage extending to 20+ more through additional non-reasoning instruction data. The model is designed to support mathematics, science, and general reasoning tasks, as well as instruction following and multilingual open-ended generation.

This is a different model from Tiny Aya L2-Thinker, which thinks in the same language as the prompt.

Developed by: Cohere and Cohere Labs

• Point of Contact: Cohere Labs
• License: CC-BY-NC, requires also adhering to Cohere Lab's Acceptable Use Policy
• Model: tiny-aya-en-thinker
• Model Size: 3.35B
• Context Length: 32K (input + output)

For the broader Tiny Aya family, see tiny-aya-global, tiny-aya-base, tiny-aya-l2-thinker, and the Tiny Aya collection.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereLabs/tiny-aya-en-thinker"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

messages = [
    {"role": "user", "content": "Plus on m'enlève, plus je deviens grand. Qui suis-je?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=32768,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))

The model supports dual-mode reasoning. In thinking mode (default behavior), apply_chat_template(..., add_generation_prompt=True, enable_thinking=True) produces a prompt of this shape:

<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble
+ developer preamble...
<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>[USER MESSAGE] /think<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>

The model then writes its thinking trace between <|START_THINKING|> and <|END_THINKING|>, followed by the user-facing answer between <|START_RESPONSE|> and <|END_RESPONSE|>. The answer is typically in the same language as the prompt.

To skip reasoning and get an answer directly, pass enable_thinking=False. The user turn is suffixed with /no_think and the generation prompt closes with an empty thinking block so the model starts at the response:

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble
+ developer preamble...
<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>[USER MESSAGE] /no_think<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|><|END_THINKING|>

You can also pass prior thinking back into the conversation:

messages = [
    {"role": "user", "content": "¿Cuántas r hay en strawberry?"},
    {
        "role": "assistant",
        "thinking": "Count the letters: S-T-R-A-W-B-E-R-R-Y. Three r's.",
        "content": "Hay 3 r en strawberry.",
    },
    {"role": "user", "content": "Ahora haz lo mismo con blueberry."},
]

The model can also be used directly using transformers pipeline abstraction:

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="CohereLabs/tiny-aya-en-thinker",
    torch_dtype="auto",
    device_map="auto",
)

print(pipe(
    [{"role": "user", "content": "Describe a home made recipe that you like most."}],
    max_new_tokens=512,
)[0]["generated_text"][-1])

Chat template behavior

The tokenizer chat template:

1. Inserts the Tiny Aya system prompt:

# System Preamble
You are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.

Your information cutoff date is June 2024.

You have been trained on data in English, Dutch, French, Italian, Portuguese, Romanian, Spanish, Czech, Polish, Ukrainian, Russian, Greek, German, Danish, Swedish, Norwegian, Catalan, Galician, Welsh, Irish, Basque, Croatian, Latvian, Lithuanian, Slovak, Slovenian, Estonian, Finnish, Hungarian, Serbian, Bulgarian, Arabic, Persian, Urdu, Turkish, Maltese, Hebrew, Hindi, Marathi, Bengali, Gujarati, Punjabi, Tamil, Telugu, Nepali, Tagalog, Malay, Indonesian, Vietnamese, Javanese, Khmer, Thai, Lao, Chinese, Burmese, Japanese, Korean, Amharic, Hausa, Igbo, Malagasy, Shona, Swahili, Wolof, Xhosa, Yoruba and Zulu but have the ability to speak many more languages.

# Default Preamble
The following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.
- Your name is Aya.
- You are a large language model built by Cohere.
- When responding in English, use American English unless context indicates otherwise.
- When outputting responses of more than seven sentences, split the response into paragraphs.
- Prefer the active voice.
- Use gender-neutral pronouns for unspecified persons.
- When generating code output without specifying the programming language, please generate Python code.

2. Appends /think to every user turn by default (enable_thinking=True). Pass enable_thinking=False to append /no_think instead.
3. With add_generation_prompt=True, appends <|START_THINKING|> to start a thinking trace, or <|START_THINKING|><|END_THINKING|> when enable_thinking=False so the model writes the answer without thinking. If an assistant message includes a thinking field, that history is also wrapped in <|START_THINKING|> / <|END_THINKING|>.

Model Details

Input: Text only.

Output: Model generates text, including an explicit English thinking trace if thinking is enabled, and a multilingual final answer.

Model Architecture: Auto-regressive transformer in the Tiny Aya / Cohere family. After pretraining, this checkpoint is supervised-fine-tuned on a mix of English reasoning traces and multilingual translated reasoning where the prompt and response are in the target language and the thinking stays in English.

Languages covered: Amharic, Arabic, Basque, Bengali, Bulgarian, Catalan, Chinese, Czech, English, Filipino, Finnish, French, German, Greek, Hausa, Hebrew, Hindi, Hungarian, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Khmer, Korean, Lithuanian, Malay, Maltese, Norwegian, Persian, Polish, Punjabi, Russian, Slovak, Swahili, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Yoruba, and Zulu.

Context Length: Tiny Aya En-Thinker supports a context length of 32K.

Usage and Limitations

Intended Usage

Tiny Aya En-Thinker is meant for multilingual conversational use when you want an English chain-of-thought and a final answer in the user's language. Intended applications include multilingual math and reasoning, open-ended generation, and research that compares English thinking to target-language reasoning.

Use Tiny Aya L2-Thinker instead if the thinking trace should stay in the prompt language.

Limitations

As with any language model, outputs may contain incorrect or outdated statements. Thinking traces can be long; cap max_new_tokens appropriately. Lowest-resource languages may show more variability than high-resource ones.

Citation

If you use this model, please cite:

@misc{mofakhami2026buildingmultilingualbridgesdata,
      title={Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning},
      author={Mehrnaz Mofakhami and Ananya Sahu and Alejandro R. Salamanca and Daniel D'souza and Alexandre Berard and Thomas Euyang and Marzieh Fadaee and Julia Kreutzer},
      year={2026},
      eprint={2609.10445},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.10445},
}

Model Card Contact

For errors or additional questions about details in this model card, contact labs@cohere.com.

Terms of Use

This model is governed by a CC-BY-NC License (Non-Commercial) and also requires adhering to Cohere Lab's Acceptable Use Policy. If you are interested in commercial use, please contact Cohere’s Sales team.