---
language:
- 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
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-generation
tags:
- aya
- multilingual
- reasoning
- tiny-aya
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thumbnail: https://lh3.googleusercontent.com/d/1NYx33YkrJgTJMa1yF5Y1MLDN1G31Ttb9

---

# Model Card for Tiny Aya L2-Thinker

## Model Summary

Cohere Labs Tiny Aya L2-Thinker is an open-weights research release of a 3.35 billion parameter multilingual reasoning model optimized to **think in the same language as the user prompt** before writing the final answer. It is trained to support in-language reasoning 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 En-Thinker](https://huggingface.co/CohereLabs/tiny-aya-en-thinker), which thinks in English.

Developed by: [Cohere](https://cohere.com/) and [Cohere Labs](https://cohere.com/research)

- Point of Contact: [Cohere Labs](https://cohere.com/research)
- License: [CC-BY-NC](https://cohere.com/cohere-labs-cc-by-nc-license), requires also adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy)
- Model: tiny-aya-l2-thinker
- Model Size: 3.35B
- Context Length: 32K (input + output)

For the broader Tiny Aya family, see [tiny-aya-global](https://huggingface.co/CohereLabs/tiny-aya-global), [tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base), and the [Tiny Aya collection](https://huggingface.co/collections/CohereLabs/tiny-aya).

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereLabs/tiny-aya-l2-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|>Think in the same language as the prompt. [USER MESSAGE] /think<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>
```

The model then writes a thinking trace between `<|START_THINKING|>` and `<|END_THINKING|>`, followed by the user-facing answer between `<|START_RESPONSE|>` and `<|END_RESPONSE|>`.

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:

```python
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|>Think in the same language as the prompt. [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:

```python
messages = [
    {"role": "user", "content": "How many r's are there in strawberry?"},
    {
        "role": "assistant",
        "thinking": "Count the letters: S-T-R-A-W-B-E-R-R-Y. Three r's.",
        "content": "There are 3 r's in strawberry.",
    },
    {"role": "user", "content": "Now do the same for blueberry."},
]
```

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

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="CohereLabs/tiny-aya-l2-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. Prefixes every user turn with `Think in the same language as the prompt. ` simulating training data.
3. Appends ` /think` to every user turn by default (`enable_thinking=True`). Pass `enable_thinking=False` to append ` /no_think` instead.
4. 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 thinking trace if thinking is enabled.

**Model Architecture:** Auto-regressive transformer in the Tiny Aya / Cohere family. After pretraining, this checkpoint is supervised-fine-tuned for multilingual reasoning so that the thinking language follows the prompt language.

**Languages covered:** 44 languages plus English: 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 L2-Thinker supports a context length of 32K.

## Usage and Limitations

### Intended Usage

Tiny Aya L2-Thinker is meant for multilingual reasoning and conversational use, especially when the thinking trace should stay in the user's language rather than defaulting to English. Intended applications include multilingual math and reasoning, open-ended generation, and research on target-language reasoning.

### 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:

```bibtex
@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](mailto:labs@cohere.com).

## Terms of Use

This model is governed by a [CC-BY-NC License](https://cohere.com/cohere-labs-cc-by-nc-license) (Non-Commercial) and also requires adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy). If you are interested in commercial use, please contact Cohere’s Sales team.