# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="CohereLabs/tiny-aya-en-thinker")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages)# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CohereLabs/tiny-aya-en-thinker")
model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-en-thinker", device_map="auto")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))Acknowledge license to accept the repository
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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:
- 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.
- Appends
/thinkto every user turn by default (enable_thinking=True). Passenable_thinking=Falseto append/no_thinkinstead. - With
add_generation_prompt=True, appends<|START_THINKING|>to start a thinking trace, or<|START_THINKING|><|END_THINKING|>whenenable_thinking=Falseso the model writes the answer without thinking. If an assistant message includes athinkingfield, 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.
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.
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